[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-shawnyuen--DeepLearningInMedicalImagingAndMedicalImageAnalysis":3,"tool-shawnyuen--DeepLearningInMedicalImagingAndMedicalImageAnalysis":62},[4,18,26,35,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,2,"2026-04-10T11:39:34",[14,15,13],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":32,"last_commit_at":41,"category_tags":42,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[43,13,15,14],"插件",{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":10,"last_commit_at":50,"category_tags":51,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[52,15,13,14],"语言模型",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4292,"Deep-Live-Cam","hacksider\u002FDeep-Live-Cam","Deep-Live-Cam 是一款专注于实时换脸与视频生成的开源工具，用户仅需一张静态照片，即可通过“一键操作”实现摄像头画面的即时变脸或制作深度伪造视频。它有效解决了传统换脸技术流程繁琐、对硬件配置要求极高以及难以实时预览的痛点，让高质量的数字内容创作变得触手可及。\n\n这款工具不仅适合开发者和技术研究人员探索算法边界，更因其极简的操作逻辑（仅需三步：选脸、选摄像头、启动），广泛适用于普通用户、内容创作者、设计师及直播主播。无论是为了动画角色定制、服装展示模特替换，还是制作趣味短视频和直播互动，Deep-Live-Cam 都能提供流畅的支持。\n\n其核心技术亮点在于强大的实时处理能力，支持口型遮罩（Mouth Mask）以保留使用者原始的嘴部动作，确保表情自然精准；同时具备“人脸映射”功能，可同时对画面中的多个主体应用不同面孔。此外，项目内置了严格的内容安全过滤机制，自动拦截涉及裸露、暴力等不当素材，并倡导用户在获得授权及明确标注的前提下合规使用，体现了技术发展与伦理责任的平衡。",88924,"2026-04-06T03:28:53",[14,15,13,61],"视频",{"id":63,"github_repo":64,"name":65,"description_en":66,"description_zh":67,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":66,"owner_location":77,"owner_email":78,"owner_twitter":66,"owner_website":79,"owner_url":80,"languages":66,"stars":81,"forks":82,"last_commit_at":83,"license":66,"difficulty_score":84,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":90,"github_topics":66,"view_count":32,"oss_zip_url":66,"oss_zip_packed_at":66,"status":17,"created_at":92,"updated_at":93,"faqs":94,"releases":95},6371,"shawnyuen\u002FDeepLearningInMedicalImagingAndMedicalImageAnalysis","DeepLearningInMedicalImagingAndMedicalImageAnalysis",null,"DeepLearningInMedicalImagingAndMedicalImageAnalysis 并非一个可直接运行的软件工具，而是一个精心整理的学术资源库，专注于深度学习在医学影像分析领域的综述与调研。它系统性地汇集了从 2016 年至 2020 年间发表的大量高质量论文，涵盖图像分割、配准、诊断、生成对抗网络（GANs）应用以及特定场景如肿瘤检测、超声分析和病理计算等核心议题。\n\n该资源库主要解决了研究人员和开发者在面对海量且分散的医学 AI 文献时，难以快速把握技术脉络、追踪最新进展及理解特定算法应用场景的痛点。通过分类整理关于数据获取、多模态融合、不完全数据集处理等关键挑战的权威综述，它为读者提供了一条清晰的学习与研究路径。\n\n这里特别适合医学影像领域的科研人员、算法工程师以及生物医学工程专业的学生使用。无论是希望入门该交叉学科的新手，还是需要撰写文献综述或寻找技术突破点的资深专家，都能从中获益。其独特亮点在于不仅关注通用深度学习模型，还深入探讨了结合领域知识的驱动策略及针对 COVID-19 等突发公共卫生事件的即时技术分析，是连接医学需求与前沿算法的重要桥梁。","# Deep Learning in Medical Imaging and Medical Image Analysis\n## Review and Survey\n### Guest Editorial Deep Learning in Medical Imaging Overview and Future Promise of an Exciting New Technique 2016 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7463094\u002F)\n### Overview of Deep Learning in Medical Imaging 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12194-017-0406-5)\n### A Survey on Deep Learning in Medical Image Analysis 2017 [[paper]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841517301135)\n### Deep Learning Applications in Medical Image Analysis 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8241753\u002F)\n### Deep Learning in Medical Image Analysis 2017 [[paper]](http:\u002F\u002Fwww.annualreviews.org\u002Fdoi\u002F10.1146\u002Fannurev-bioeng-071516-044442)\n### Deep Learning in Microscopy Image Analysis A Survey 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8118310\u002F)\n### GANs for Medical Image Analysis arXiv 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06222)\n### Generative Adversarial Network in Medical Imaging: A Review arXiv 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07294)\n### Deep Learning in Medical Image Registration: A Survey arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02026)\n### Deep Learning in Medical Image Registration: A Review arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12318)\n### Deep Learning in Medical Ultrasound Analysis A Review Engineering 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2095809918301887)\n### Deep Learning in Cardiology arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11122)\n### Deep learning in Medical Imaging and Radiation Therapy MP 2019 [[paper]](https:\u002F\u002Faapm.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmp.13264)\n### Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges JDI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-019-00227-x)\n### Embracing Imperfect Datasets A Review of Deep Learning Solutions for Medical Image Segmentation MedIA 2020 [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.10454) [[MedIA paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS136184152030058X)\n### Machine Learning Techniques for Biomedical Image Segmentation An Overview of Technical Aspects and Introduction to State-of-Art Applications arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02521)\n### Deep Neural Network Models for Computational Histopathology A Survey arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12378)\n### A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12150)\n### State-of-the-Art Deep Learning in Cardiovascular Image Analysis JACC 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1936878X19305753)\n### A Review of Deep Learning in Medical Imaging Image Traits Technology Trends Case Studies with Progress Highlights and Future Promises arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.09104)\n### Review of Artificial Intelligence Techniques in Imaging Data Acquisition Segmentation and Diagnosis for COVID-19 IEEE RBME 2020 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9069255)\n### Model-Based and Data-Driven Strategies in Medical Image Computing IEEE Proceedings 2020 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8867900) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.10391)\n### Deep Learning Based Brain Tumor Segmentation A Survey arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09479)\n### A Review Deep Learning for Medical Image Segmentation Using Multi-modality Fusion arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10664)\n### Medical Instrument Detection in Ultrasound-Guided Interventions A Review arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04807)\n### A Review of Deep Learning in Medical Imaging Image Traits Technology Trends Case Studies with Progress Highlights and Future Promises arXiv 2020 [[paper]]()\n### Medical Image Segmentation Using Deep Learning A Survey arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13120)\n### Learning-based Algorithms for Vessel Tracking A Review arXiv 2020 [[paper]]()\n### Deep Learning for Cardiac Image Segmentation A Review FCVM 2020 [[paper]](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffcvm.2020.00025\u002Ffull)\n### Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology Circulation 2020 [[paper]](https:\u002F\u002Fwww.ahajournals.org\u002Fdoi\u002Ffull\u002F10.1161\u002FCIRCEP.119.007952)\n### Overview of the Whole Heart and Heart Chamber Segmentation Methods CET 2020 [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13239-020-00494-8)\n### Deep Learning for Chest X-ray Analysis A Survey arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.08700)\n### Multi-Modality Cardiac Image Computing A Survey arXiv 2022 [[paper]]()\n### Nuclei & Glands Instance Segmentation in Histology Images A Narrative Review arXiv 2022 [[paper]]()\n\n## Datasets\n### Development of a Digital Image Database for Chest Radiographs with and without a Lung Nodule AJR 2000\n\"Chest Radiographs\", \"the JSRT database\"\n### Segmentation of Anatomical Structures in Chest Radiographs Using Supervised Methods A Comparative Study on a Public Database MedIA 2006\n\"Chest Radiographs\", \"the SCR dataset (ground-truth segmentation masks) for the JSRT database (X-ray images)\"\n### ChestX-ray8 Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases CVPR 2017 [[dataset]](https:\u002F\u002Fnihcc.app.box.com\u002Fv\u002FChestXray-NIHCC)\n\"Chest Radiographs\"\n### KiTS 2019 [[dataset]](https:\u002F\u002Fgithub.com\u002Fneheller\u002Fkits19)\n\"300 Abdomen CT scans for kidney and tumor segmentation\"\n### CHD_Segmentation [[dataset]](https:\u002F\u002Fgithub.com\u002FXiaoweiXu\u002FWhole-heart-and-great-vessel-segmentation-of-chd_segmentation\u002Ftree\u002Fmaster)\n\"68 CT images with labels. The label includes left ventricle, right ventricle, left atrium, right atrium, myocardium, aorta, and pulmonary artery.\"\n### Skin Lesion Analysis Toward Melanoma Detection 2018 A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) arXiv 2019\n### ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection arXiv 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00523)\n\"ISIC2016\", \"ISIC2017\", \"ISIC2018\", \"ISIC2019\"\n### VerSe A Vertebrae Labelling and Segmentation Benchmark arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.09193)\n\"VerSe\"\n### A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology IEEE TMI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7872382)\n### A Multi-Organ Nucleus Segmentation Challenge IEEE TMI 2020 [[paper]]()\n\"MoNuSeg\"\n### Deep Learning to Segment Pelvic Bones Large-scale CT Datasets and Baseline Models arXiv 2020 [[paper]]()\n\"CTPelvic1K\"\n### RibSeg v2 A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction arXiv 2022 [[paper]]()\n\"RibSeg\"\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Computed Tomography (CT)\n## 2022\n### Learning Topological Interactions for Multi-Class Medical Image Segmentation ECCV Oral 2022 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09654) [[code]](https:\u002F\u002Fgithub.com\u002FTopoXLab\u002FTopoInteraction)\n\n## 2015\n### 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data MICCAI 2015 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-24553-9_69)\n\n## 2016\n### An Artificial Agent for Anatomical Landmark Detection in Medical Images MICCAI 2016 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46726-9_27)\n\"deep reinforcement learning\", \"anatomical landmark detection\"\n### Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields MICCAI 2016 [[paper]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_48)\n\"CRF\"\n### Low-dose CT Denoising with Convolutional Neural Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.00321)\n### Low-Dose CT via Deep Neural Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.08508)\n### Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7422783\u002F)\n### Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation IEEE TMI 2016 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7279156)\n\n## 2017\n### Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00961)\n### Automatic Liver Segmentation Using an Adversarial Image-to-Image Network MICCAI 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08037)\n### Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.06453)\n### Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.08333)\n### Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image [[paepr]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02073)\n### A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02764)\n### DeepLesion Automated Deep Mining Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.01766)\n### Unsupervised End-to-end Learning for Deformable Medical Image Registration [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08608)\n### DeepLung 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.05538)\n### CT Image Denoising with Perceptive Deep Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07019)\n### Improving Low-Dose CT Image Using Residual Convolutional Network [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8082505\u002F)\n### Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7947200\u002F)\n### Stacked Competitive Networks for Noise Reduction in Low-dose CT [[paper]](http:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0190069)\n### Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08324)\n### Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-42999-1_4)\n### Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66182-7_23)\n### Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans TPAMI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8187667\u002F)\n### 3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images MedIA 2017 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841517300725)\n\"deep supervision mechanism\"\n### Generative Adversarial Networks for Noise Reduction in Low-Dose CT IEEE TMI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7934380)\n\n## 2018\n### A Two-stage 3D Unet Framework for Multi-class Segmentation on Full Resolution Image arXiv 2018[[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04341)\n### DeepLung Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09555)\n### Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08599)\n### Attention U-Net Learning Where to Look for the Pancreas [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03999)\n### 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.05656)\n### Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8332971\u002F)\n### Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00587)\n### Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data MedIA 2018 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518304092)\n### Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images Arxiv 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02908)\n\"reinforcement learning\", \"anatomical landmark localization\", \"aortic valve\". \"left atrial appendage\"\n### Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00739)\n### Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network CVPR 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09655)\n### AnatomyNet Deep 3D Squeeze-and-excitation U-Nets for Fast and Fully Automated Whole-volume Anatomical Segmentation Medical Physics 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05238)\n### DeepEM Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05373)\n### Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02268)\n### Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01307)\n### Deep Learning Based Rib Centerline Extraction and Labeling [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07082)\n### Liver Lesion Detection from Weakly-Labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector MICCAI 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00934-2_77)\n### CFUN Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.04914)\n### Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database CVPR 2018 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fhtml\u002FYan_Deep_Lesion_Graphs_CVPR_2018_paper.html)\n### 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas CR 2018 [[paper]](http:\u002F\u002Fcancerres.aacrjournals.org\u002Fcontent\u002F78\u002F24\u002F6881.short)\n### (AH-Net) 3D Anisotropic Hybrid Network Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes MICCAI 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00934-2_94)\n\"liver and liver tumor segmentation from a Computed Tomography volume\", \"lesion detection from a Digital Breast Tomosynthesis volume\"\n### 3D U-JAPA-Net Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation MICCAI 2018 [[paper]]()\n### A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation MICCAI 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00937-3_48)\n### Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs IJCARS 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11548-018-1884-6)\n\n## 2019\n### 3DFPN-HS2 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection MICCAI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32226-7_57)\n### A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography IEEE TMI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8550784)\n### Abdominal Multi-organ Segmentation with Organ-attention Networks and Statistical Fusion MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518302524)\n### Attention Gated Networks Learning to Leverage Salient Regions in Medical Images MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518306133)\n### Automated Coronary Artery Atherosclerosis Detection and Weakly Supervised Localization on Coronary CT Angiography with a Deep 3-Dimensional Convolutional Neural Network arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.13219) [[CMIG paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0895611120300240)\n### Automated Design of Deep Learning Methods for Biomedical Image Segmentation arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.08128)\n### Combined Analysis of Coronary Arteries and the Left Ventricular Myocardium in Cardiac CT Angiography for Detection of Patients with Functionally Significant Stenosis arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04940)\n### Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-based Orientation Classifier MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518308491) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.03143)\n### Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics MICCAI 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06075)\n### Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06417)\n### Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT IEEE TMI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8643342)\n### Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography MICCAI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32245-8_52)\n### Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network IEEE TMI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8625393)\n### Motion Artifact Recognition and Quantification in Coronary CT Angiography Using Convolutional Neural Networks MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518308624)\n### Motion Estimation and Correction in Cardiac CT Angiography Images Using Convolutional Neural Networks CMIG 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0895611119300515)\n\n## 2020\n### 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03739)\n### Bounding Maps for Universal Lesion Detection arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09383)\n### C2FNAS Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation CVPR 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.09628)\n### Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.05393) [[code]](https:\u002F\u002Fgithub.com\u002FShawnBIT\u002FBrain-Midline-Detection)\n### CPR-GCN Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries CVPR 2020 [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FYang_CPR-GCN_Conditional_Partial-Residual_Graph_Convolutional_Network_in_Automated_Anatomical_Labeling_CVPR_2020_paper.html)\n### Deep Distance Transform for Tubular Structure Segmentation in CT Scans CVPR 2020 [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FWang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html)\n\"\"\n### Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography IEEE TMI 2020 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8896989)\n### Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07469)\n### Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.04207)\n\"textures and edge information\"\n### Going to Extremes Weakly Supervised Medical Image Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.11988)\n### Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling MLMI 2020 [[paper]]()\n### Learning Metal Artifact Reduction in Cardiac CT Images with Moving Pacemakers MedIA 2020 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841520300220)\n### Modified U-Net (mU-Net) with Incorporation of Object-dependent High Level Features for Improved Liver and Liver-tumor Segmentation in CT Images IEEE TMI 2020 [[paper]]()\n### Multi-resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00925)\n\"improvement of CNN-based Orientation Classifier (vessel tracker)\"\n### Multi-view Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at Risk in Head and Neck CT Images IEEE TMI 2020 [[paper]]()\n### One Click Lesion RECIST Measurement and Segmentation on CT Scans arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.11087)\n### PGL Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.12640)\n### RA-UNet A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans 2020\n### Rapid Vessel Segmentation and Reconstruction of Head and Neck Angiograms Using 3D Convolutional Neural Network NC 2020 [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-18606-2)\n### SenseCare A Research Platform for Medical Image Informatics and Interactive 3D Visualization arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.07031)\n### TopNet Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08674)\n### TripletUNet Multi-Task U-Net with Online Voxel-Wise Learning for Precise CT Prostate Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07462)\n### UXNet Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07501)\n\n## 2021\n### Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01897)\n### CoTr Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03024)\n### Swin-Unet Unet-like Pure Transformer for Medical Image Segmentation arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05537)\n### Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01315)\n\n## 2022\n### Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers arXiv 2022 [[paper]]()\n### Boundary-Aware Network for Abdominal Multi-Organ Segmentation arXiv 2022 [[paper]]()\n### Boundary-Aware Network for Kidney Parsing arXiv 2022 [[paper]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Magnetic Resonance Imaging (MRI)\n## 2022\n### (RefSeg) Online Reflective Learning for Robust Medical Image Segmentation MICCAI 2022 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.00476)\n\n## 2015\n### Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation MICCAI 2015 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24574-4_1)\n\n## 2016\n### Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-55050-3_8)\n### Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks MICCAI 2016 [[paper]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_51)\n\"CRF\"\n### Regressing Heatmaps for Multiple Landmark Localization Using CNNs MICCAI 2016 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_27)\n\"Multiple Landmark Localization\"\n\n## 2017\n### SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01805)\n### Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01141)\n### Deep MR to CT Synthesis using Unpaired Data [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01155)\n### Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00983)\n### 3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study [[paper]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1053811917303324) [[code]](https:\u002F\u002Fgithub.com\u002Fjosedolz\u002FLiviaNET)\n### 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09813)\n### Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets\n### Deep Generative Adversarial Networks for Compressed Sensing Automates MRI [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00051)\n### Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.09300)\n### Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00382)\n### Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.01135)\n### A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067520\u002F)\n### Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00753)\n### Learning a Variational Network for Reconstruction of Accelerated MRI Data [[paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmrm.26977)\n### A Parallel MR Imaging Method Using Multilayer Perceptron [[paper]](https:\u002F\u002Faapm.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmp.12600)\n### A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067520\u002F)\n### Image Reconstruction by Domain Transform Manifold Learning [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.08841)\n### Human-level CMR Image Analysis with Deep Fully Convolutional Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09289)\n### A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue MICCAI 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66185-8_79)\n\"CRF\"\n### Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation MICCAI 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66185-8_49)\n\"CRF\"\n### Medical Image Synthesis with Context-aware Generative Adversarial Networks MICCAI 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-66179-7_48) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05362)\n\n## 2018\n### Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02728)\n### 3D Multi-scale FCN with Random Modality Voxel Dropout Learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518300136)\n### Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01417)\n### Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00432)\n### k-Space Deep Learning for Accelerated MRI [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03779)\n### Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01200)\n### Deformable Image Registration Using a Cue-Aware Deep Regression Network TBME 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8331111\u002F)\n### Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images TBME 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067513\u002F)\n### 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00076)\n\"focal loss\", \"Exponential Logarithmic Loss\"\n### Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-662-56537-7_89)\n### An Unsupervised Learning Model for Deformable Medical Image Registration CVPR 2018 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fhtml\u002FBalakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html)\n### VoxelMorph: A Learning Framework for Deformable Medical Image Registration IEEE TMI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05231)\n### Direct Delineation of Myocardial Infarction without Contrast Agents Using a Joint Motion Feature Learning Architecture MedIA 2018 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518306960)\n### Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation IEEE TMI 2018 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051114\u002F)\n### Towards MR-Only Radiotherapy Treatment Planning: Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks MICCAI 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00928-1_33)\n### Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN DLMIA 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00889-5_20) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04536)\n\n## 2019\n### A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation MICCAI 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.06148) [[code]](https:\u002F\u002Fgithub.com\u002FRobinBruegger\u002FPartiallyReversibleUnet)\n### Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network IEEE TMI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8447517)\n\n## 2020\n### Automated Intracranial Artery Labeling Using a Graph Neural Network and Hierarchical Refinement MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14472)\n### Brain Tumor Segmentation Using 3D-CNNs with Uncertainty Estimation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.12188)\n### CA-Net Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.10549)\n### (CANet) CANet Context Aware Network for 3D Brain Tumor Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07788)\n### Cardiac Segmentation with Strong Anatomical Guarantees arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08825)\n### CS2-Net Deep Learning Segmentation of Curvilinear Structures in Medical Imaging arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.07486)\n### Deep Morphological Simplification Network MS-Net for Guided Registration of Brain Magnetic Resonance Images PR 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320319304716) [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.02342)\n### Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.12111)\n### Knowledge Distillation for Brain Tumor Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.03688)\n### MS-Net Multi-site Network for Improving Prostate Segmentation with Heterogeneous MRI Data IEEE TMI 2020 [[paper]]()\n### Optimization for Medical Image Segmentation Theory and Practice When Evaluating with Dice Score or Jaccard Index IEEE TMI 2020 [[paper]]()\n### (AsynDGAN) Synthetic Learning Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data CVPR 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00080)\n\"AsynDGAN is comprised of one central generator and multiple distributed discriminators located in different medical entities.\"\n### Two-Stage Cascaded U-Net 1st Place Solution to BraTS Challenge 2019 Segmentation Task BrainLes 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-46640-4_22)\n### UNet++ Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation IEEE TMI 2020 [[paper]]()\n### ψ-Net Stacking Densely Convolutional LSTMs for Sub-cortical Brain Structure Segmentation IEEE TMI 2020 [[paper]]()\n\n## 2021\n### TransBTS Multimodal Brain Tumor Segmentation Using Transformer arXiv 2021 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04430) [[PyTorch code]](https:\u002F\u002Fgithub.com\u002FWenxuan-1119\u002FTransBTS)\n\n## 2022\n### Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis arXiv 2022 [[paper]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Ultrasound (US)\n## 2015\n### Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks MICCAI 2015 [[paper]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24553-9_62)\n### Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks IEEE JBHI 2015 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7090943)\n\n## 2016\n### Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231216002344)\n### Real-time Detection and Localisation of Fetal Standard Scan Planes in 2D Freehand Ultrasound 2016 [[paper]]()\n### Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks 2016 [[paper]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_24)\n### Describing Ultrasound Video Content Using Deep Convolutional Neural Networks 2016 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493384\u002F)\n\n## 2017\n### Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning [[paepr]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00712)\n### Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05392)\n### Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07881)\n### Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051114\u002F)\n### Hough-CNN Deep learning for segmentation of deep brain regions in MRI and ultrasound CVIU 2017 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1077314217300620)\n### Cascaded Fully Convolutional Networks for Automatic Prenatal Ultrasound Image Segmentation 2017 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950607\u002F)\n### Ultrasound Standard Plane Detection Using a Composite Neural Network Framework 2017 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7890445\u002F)\n### CNN-based Estimation of Abdominal Circumference from Ultrasound Images 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.02741)\n### Ultrasound Image-based Thyroid Nodule Automatic Segmentation Using Convolutional Neural Networks IJCARS 2017 [[paper]]\n\"thyroid\"\n### SonoNet Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound IEEE TMI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7974824) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05601)\n\n## 2018\n### A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification IEEE TBME 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8372445\u002F)\n### Adversarial Image Registration with Application for MR and TRUS Image Fusion 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.11024)\n### Attention-Gated Networks for Improving Ultrasound Scan Plane Detection 2018 [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=BJtn7-3sM)\n### Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7927411\u002F)\n### Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound [[paepr]](https:\u002F\u002Fopenreview.net\u002Fforum?id=r1ZGQW2if)\n### Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10737)\n### Fast Multiple Landmark Localisation Using a Patch-based Iterative Network MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06987) [[TF code]](https:\u002F\u002Fgithub.com\u002Fyuanwei1989\u002Flandmark-detection)\n### Fully-automated Alignment of 3D Fetal Brain Ultrasound to a Canonical Reference Space Using Multi-task Learning MedIA 2018 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518300306)\n### Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051098\u002F)\n### High Frame-rate Cardiac Ultrasound Imaging with Deep Learning MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07823)\n### High Quality Ultrasonic Multi-line Transmission through Deep Learning MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07819)\n### Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.09102)\n### Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks CR 2018 [[paper]](http:\u002F\u002Fcancerres.aacrjournals.org\u002Fcontent\u002F78\u002F17\u002F5135.short)\n### Less is More Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10376)\n### Multi-task SonoEyeNet Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps MICCAI 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00928-1_98)\n### Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07486)\n### Weakly Supervised Localisation for Fetal Ultrasound Images DLMIAW 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00793)\n\n## 2019\n### Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation IEEE TMI 2018 [[paper]](Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation)\n### Automated Detection and Classification of Thyroid Nodules in Ultrasound Images Using Clinical-knowledge-guided Convolutional Neural Networks MedIA 2019 [[paper]]()\n\"thyroid\"\n\n## 2020\n### Contrastive Rendering for Ultrasound Image Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.04928)\n### Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network IEEE TBME 2020 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8698332)\n### Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging IEEE TMI 2020 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8896024)\n### Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.10732) [[code]](https:\u002F\u002Fgithub.com\u002Fkleinzcy\u002FSASSnet)\n### Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis IEEE TMI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.08698) [[code]](https:\u002F\u002Fbitbucket.org\u002FJianboJiao\u002Fssus2mri\u002Fsrc)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# X-ray\n## 2015\n### Deep Learning and Structured Prediction for the Segmentation of Mass in Mamograms MICCAI 2015 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24553-9_74)\n\n## 2016\n### Learning to Read Chest X-Rays Recurrent Neural Cascade Model for Automated Image Annotation 2016 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.08486)\n\n## 2017\n### Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks DLMIA 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00710)\n### Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09850)\n### Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05053)\n\"reimplement this recently\", \"segmentation data for normalization was done\"\n### Cascade of Multi-scale Convolutional Neural Networks for Bone Suppression of Chest Radiographs in Gradient Domain 2017 [[paper]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841516301529)\n### CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05225)\n### Adversarial Deep Structural Networks for Mammographic Mass Segmentation MICCAI 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05970)\n### Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification MICCAI 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66179-7_69)\n### A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification 2017 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-67558-9_20)\n### High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07047)\n### Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning TMI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8032490\u002F)\n### Deep Learning for Automated Skeletal Bone Age Assessment in X-ray Images MedIA 2017\n\"focus on this recently (20181001)\"\n\n## 2018\n### SCAN Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJ1RffhjM)\n### Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs IEEE TMI 2018 [[TMI paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8302848\u002F) [[ArXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.08816)\n### Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00600)\n### LF-SegNet A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11277-018-5702-9)\n### Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks 2018 [[paper]]()\n### Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.02315)\n### Breast Mass Segmentation and Shape Classification in Mammograms Using Deep Neural Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01687)\n\"conditional generative adversarial networks\", \"INbreast\", \"digital database for screening mammography (DDSM)\"\n### Medical Image Description Using Multi-task-loss CNN 2016 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46976-8_13)\n### Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10207)\n### Benign and malignant breast tumors classification based on region growing and CNN segmentation ESA 2015 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417414005594)\n### Adversarial Deep Structured Nets for Mass Segmentation from Mammograms ISBI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09288)\n### Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.08885)\n### Thoracic Disease Identification and Localization with Limited Supervision CVPR 2018 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers_backup\u002FLi_Thoracic_Disease_Identification_CVPR_2018_paper.pdf)\n### Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07703)\n### Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning CBM 2018 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0010482518300799)\n### Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder RAMBO 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.02113)\n\n## 2019\n### Accurate Automated Cobb Angles Estimation Using Multi-view Extrapolation Net MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300775)\n### Learning to Detect Chest Radiographs Containing Pulmonary Lesions Using Visual Attention Networks MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518304997)\n### When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification IEEE ISBI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8759510)\n\n## 2020\n### High-resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors IEEE TMI 2020 [[paper]]()\n### Image-to-Images Translation for Multi-task Organ Segmentation and Bone Suppression in Chest X-ray Radiography IEEE TMI 2020 [[paper]]()\n### Vertebra-focused Landmark Detection for Scoliosis Assessment IEEE ISBI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.03187)\n\n## 2021\n### Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease arXiv 2021 [[paper]]()\n### Seg4Reg+ Consistency Learning between Spine Segmentation and Cobb Angle Regression MICCAI 2021 [[paper]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n\n# Positron Emission Tomography (PET)\n## 2017\n### Combo Loss Handling Input and Output Imbalance in Multi-Organ Segmentation arXiv 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02798)\n### Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results arXiv 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09585)\n\n## 2018\n### Iterative PET Image Reconstruction Using Convolutional Neural Network Representation IEEE TMI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8463596)\n### PET Image Reconstruction Using Deep Image Prior IEEE TMI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8581448)\n\n## 2019\n### Cross-modality Synthesis from CT to PET Using FCN and GAN Networks for Improved Automated Lesion Detection ENGAPPAI 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0952197618302513)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Funduscopy\n## 2016\n### DeepVessel Retinal Vessel Segmentation via Deep Learning and Conditional Random Field MICCAI 2016 [[paper]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_16)\n\"CRF\"\n\n## 2017\n### Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09318) [[Keras+TF code]](https:\u002F\u002Fbitbucket.org\u002Fwoalsdnd\u002Fv-gan)\n### Towards Adversarial Retinal Image Synthesis arXiv 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.08974) [[code]](https:\u002F\u002Fgithub.com\u002Fcostapt\u002Fvess2ret)\n\n## 2018\n### End-to-End Adversarial Retinal Image Synthesis IEEE TMI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8055572) [[code]](https:\u002F\u002Fgithub.com\u002Fcostapt\u002Fadversarial_retinal_synthesis)\n### Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation TMI 2018 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8252743\u002F)\n### Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation TBME 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8341481\u002F)\n\n## 2019\n### CE-Net: Context Encoder Network for 2D Medical Image Segmentation IEEE TMI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8662594)\n### Deep Vessel Segmentation by Learning Graphical Connectivity MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300982) [[TF code]](https:\u002F\u002Fgithub.com\u002Fsyshin1014\u002FVGN)\n\n## 2020\n### Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation arXiv 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07476)\n\"IVUS images are similar to Eye Fundus Images.\"\n\n----------------------------------------------------------------------------------------------------------------------------------------\n#  Microscopy\n## 2016\n### Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology [[paper]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0895611116300404)\n### Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images IEEE TMI 2016 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7163353\u002F)\n\n## 2017\n### Adversarial Image Alignment and Interpolation [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00067)\n### CNN Cascades for Segmenting Whole Slide Images of the Kidney [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00251)\n### Learning to Segment Breast Biopsy Whole Slide Images [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02554)\n### SFCN-OPI Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.08297)\n### MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network CVPR 2017 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FZhang_MDNet_A_Semantically_CVPR_2017_paper.pdf)\n\n## 2018\n### Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification ICIAR 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00931)\n### Cancer Metastasis Detection With Neural Conditional Random Field MIDL 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07064)\n### DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks MedIA 2018 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841517301834)\n\n## 2019\n### Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation MICCAI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-32245-8_12) [[arXiv paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.11524)\n### HoVer-Net Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519301045)\n### Weakly supervised mitosis detection in breast histopathology images using concentric loss MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300118)\n\n## 2020\n### Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.10787)\n### MultiStar Instance Segmentation of Overlapping Objects with Star-convex Polygons arXiv 2020 [[paper]]()\n### Nucleus Segmentation Across Imaging Experiments the 2018 Data Science Bowl NM 2020 [[paper]]()\n### Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset arXiv 2020 [[paper]]()\n\n## 2022\n### Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images MICCAI 2022 [[paper]]()\n### Region-guided CycleGANs for Stain Transfer in Whole Slide Images arXiv 2022 [[paper]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Colonoscopy\n## 2016\n### Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning TMI 2016 [[papr]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7426826\u002F)\n\n## 2018\n### Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-73603-7_32)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# OCT\n## 2017\n### Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.05324)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Dermoscopy\n## 2016\n### Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification IEEE ISBI 2016 [[paepr]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493447\u002F)\n### Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950524\u002F)\n### Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493447\u002F)\n\n## 2017\n### Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks IEEE TMI 2017 [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7792699\u002F)\n### Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7903636\u002F)\n\"Jaccard distance on one hand, is similar to the known Dice overlap coefficient (also a novel loss function in V-Net), on the other hand, in the above paper, is a novel loss function suitable for binary class segmentation task. obviously, Jaccard distance is similar to IoU (intersection over union), a strict metric in object\u002Fsemantic segmentation in computer vision.\"\n### Investigating deep side layers for skin lesion segmentation [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950514\u002F)\n### Skin Lesion Segmentation via Deep RefineNet [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-67558-9_35)\n### Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8239798\u002F)\n### Segmentation of dermoscopy images based on fully convolutional neural network [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8296578\u002F)\n### Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10449)\n\"Multi-class (classification and segmentation)\"\n### Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8239798\u002F)\n### Dermoscopic Image Segmentation via Multi-Stage Fully Convolutional Networks [[paper]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7942129\u002F)\n### Skin Melanoma Segmentation Using Recurrent and Convolutional Neural Networks IEEE ISBI 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950522\u002F)\n### Skin Lesion Classification Using Hybrid Deep Neural Networks 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08434)\n### Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble arXiv 2017 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.03108)\n### Knowledge Transfer for Melanoma Screening with Deep Learning 2017 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950523\u002F)\n\n## 2018\n### Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features IEEE TBME 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8440053\u002F)\n### Classification for Dermoscopy Images Using Convolutional Neural Networks Based on Region Average Pooling IEEE Access 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8502872)\n### A Multi-task Framework with Feature Passing Module for Skin Lesion Classification and Segmentation IEEE ISBI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8363769\u002F)\n### Skin Lesion Analysis Toward Melanoma Detection IEEE ISBI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8363547\u002F)\n### A Deep Residual Architecture for Skin Lesion Segmentation ISIC 2018 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-01201-4_30)\n### DermoNet Densely Linked Convolutional Neural Network for Efficient Skin Lesion Segmentation [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=B167qcojM)\n### Techniques and Algorithms for Computer Aided Diagnosis of Pigmented Skin Lesions A Review [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1746809417301428)\n### MelanoGANs High Resolution Skin Lesion Synthesis with GANs [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04338)\n### SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks MICCAI 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10241)\n### Skin Lesion Classification with Ensemble of Squeeze-and-excitation Networks and Semi-supervised Learning 2018 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02568)\n\n## 2019\n### Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions CVPRW 2019 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FBarata_Deep_Attention_Model_for_the_Hierarchical_Diagnosis_of_Skin_Lesions_CVPRW_2019_paper.html)\n### DermaKNet Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis IEEE JBHI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8293766)\n### Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features IEEE JBHI 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8353143)\n### Melanoma Recognition via Visual Attention IPMI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-20351-1_62)\n### Skin Lesion Classification Using Convolutional Neural Network with Novel Regularizer IEEE Access 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8669763)\n### Solo or Ensemble Choosing a CNN Architecture for Melanoma Classification CVPRW 2019 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FPerez_Solo_or_Ensemble_Choosing_a_CNN_Architecture_for_Melanoma_Classification_CVPRW_2019_paper.html)\n### Towards Automated Melanoma Detection with Deep Learning Data Purification and Augmentation CVPRW 2019 [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FBisla_Towards_Automated_Melanoma_Detection_With_Deep_Learning_Data_Purification_and_CVPRW_2019_paper.html)\n\n## 2020\n### Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model IEEE TMI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07377)\n\"The idea may be inspired by the paper titled 'Correlation Congruence for Knowledge Distillation ICCV 2019'. \"\n### A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification IEEE TMI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.03313)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# Endoscopy\n## 2018\n### Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks IEEE TMI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8259318\u002F) [[code]](https:\u002F\u002Fgithub.com\u002Fsurgical-vision\u002FEndoVisPoseAnnotation)\n### 3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery IEEE TMI 2018 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8295119)\n## 2019\n### Quantification and Analysis of Laryngeal Closure From Endoscopic Videos IEEE TBME 2019 [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8450618)\n### Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking in surgical video MedIA 2019 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519300593)\n### Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video MICCAI 2019 [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32254-0_49)\n### 2017 Robotic Instrument Segmentation Challenge arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.06426)\n### Endoscopy artifact detection (EAD 2019) challenge dataset arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.03209)\n### A deep learning framework for quality assessment and restoration in video endoscopy arXiv 2019 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07073)\n\n## 2020\n### Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video MICCAI 2020 [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.02501) [[code]](https:\u002F\u002Fgithub.com\u002Fzxzhaoeric\u002FSemi-InstruSeg)\n### Multi-task recurrent convolutional network with correlation loss for surgical video analysis MedIA 2020 [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519301124)\n","# 医学影像中的深度学习与医学图像分析\n## 综述与调查\n### 特邀社论：医学影像中的深度学习——一项令人振奋的新技术的概述与未来前景 2016 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7463094\u002F)\n### 医学影像中的深度学习概述 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12194-017-0406-5)\n### 医学图像分析中深度学习的综述 2017 [[论文]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841517301135)\n### 医学图像分析中的深度学习应用 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8241753\u002F)\n### 医学图像分析中的深度学习 2017 [[论文]](http:\u002F\u002Fwww.annualreviews.org\u002Fdoi\u002F10.1146\u002Fannurev-bioeng-071516-044442)\n### 显微镜图像分析中的深度学习：综述 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8118310\u002F)\n### 用于医学图像分析的生成对抗网络 arXiv 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06222)\n### 医学成像中的生成对抗网络：综述 arXiv 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07294)\n### 医学图像配准中的深度学习：综述 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02026)\n### 医学图像配准中的深度学习：综述 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12318)\n### 医学超声分析中的深度学习：综述 工程 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2095809918301887)\n### 心脏病学中的深度学习 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11122)\n### 医学影像与放射治疗中的深度学习 MP 2019 [[论文]](https:\u002F\u002Faapm.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmp.13264)\n### 医学图像分割的深度学习技术：成就与挑战 JDI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-019-00227-x)\n### 拥抱不完美数据集：医学图像分割深度学习解决方案综述 MedIA 2020 [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.10454) [[MedIA论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS136184152030058X)\n### 生物医学图像分割的机器学习技术：技术要点概述及前沿应用介绍 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02521)\n### 计算组织病理学用深度神经网络模型：综述 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12378)\n### 基于领域知识的医学图像分析深度学习综述 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12150)\n### 心血管图像分析中的最先进深度学习 JACC 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1936878X19305753)\n### 医学影像中的深度学习综述：图像特征、技术趋势、案例研究，以及进展亮点与未来展望 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.09104)\n### COVID-19影像数据采集、分割和诊断中的人工智能技术综述 IEEE RBME 2020 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9069255)\n### 医学图像计算中的基于模型与数据驱动策略 IEEE会议论文 2020 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8867900) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.10391)\n### 基于深度学习的脑肿瘤分割：综述 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09479)\n### 多模态融合用于医学图像分割的深度学习综述 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10664)\n### 超声引导介入中的医疗器械检测：综述 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04807)\n### 医学影像中的深度学习综述：图像特征、技术趋势、案例研究，以及进展亮点与未来展望 arXiv 2020 [[论文]]()\n### 基于深度学习的医学图像分割：综述 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13120)\n### 基于学习的血管追踪算法：综述 arXiv 2020 [[论文]]()\n### 心脏图像分割的深度学习：综述 FCVM 2020 [[论文]](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffcvm.2020.00025\u002Ffull)\n### 心律失常与心脏电生理学中的人工智能与机器学习 Circulation 2020 [[论文]](https:\u002F\u002Fwww.ahajournals.org\u002Fdoi\u002Ffull\u002F10.1161\u002FCIRCEP.119.007952)\n### 全心及心腔分割方法概述 CET 2020 [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13239-020-00494-8)\n### 胸部X线分析的深度学习：综述 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.08700)\n### 多模态心脏图像计算：综述 arXiv 2022 [[论文]]()\n### 组织学图像中细胞核与腺体实例分割：叙事性综述 arXiv 2022 [[论文]]()\n\n## 数据集\n### 胸部X线数字图像数据库的开发，包含有无肺结节的影像 AJR 2000\n“胸部X线”，“JSRT数据库”\n### 使用监督方法对胸部X线解剖结构进行分割：基于公共数据库的比较研究 MedIA 2006\n“胸部X线”，“SCR数据集（地面真值分割掩膜）用于JSRT数据库（X线图像）”\n### ChestX-ray8 医院规模的胸部X线数据库及常见胸腔疾病的弱监督分类与定位基准 CVPR 2017 [[数据集]](https:\u002F\u002Fnihcc.app.box.com\u002Fv\u002FChestXray-NIHCC)\n“胸部X线”\n### KiTS 2019 [[数据集]](https:\u002F\u002Fgithub.com\u002Fneheller\u002Fkits19)\n“300例腹部CT扫描，用于肾脏和肿瘤分割”\n### CHD_Segmentation [[数据集]](https:\u002F\u002Fgithub.com\u002FXiaoweiXu\u002FWhole-heart-and-great-vessel-segmentation-of-chd_segmentation\u002Ftree\u002Fmaster)\n“68张带标签的CT图像。标签包括左心室、右心室、左心房、右心房、心肌、主动脉和肺动脉。”\n### 面向黑色素瘤检测的皮肤病变分析 2018 国际皮肤成像合作组织（ISIC）主办的挑战 arXiv 2019\n### ISIC 2017——面向黑色素瘤检测的皮肤病变分析 arXiv 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00523)\n“ISIC2016”、“ISIC2017”、“ISIC2018”、“ISIC2019”\n### VerSe 椎骨标注与分割基准 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.09193)\n“VerSe”\n### 用于计算病理学的广义细胞核分割数据集与技术 IEEE TMI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7872382)\n### 多器官细胞核分割挑战 IEEE TMI 2020 [[论文]]()\n“MoNuSeg”\n### 深度学习用于骨盆骨骼分割：大规模CT数据集与基线模型 arXiv 2020 [[论文]]()\n“CTPelvic1K”\n### RibSeg v2 大规模肋骨标注与解剖中心线提取基准 arXiv 2022 [[论文]]()\n“RibSeg”\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 计算机断层扫描（CT）\n## 2022\n\n### 用于多类别医学图像分割的拓扑交互学习 ECCV 口头报告 2022 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09654) [[代码]](https:\u002F\u002Fgithub.com\u002FTopoXLab\u002FTopoInteraction)\n\n## 2015\n### 用于高效稳健地从体积数据中检测解剖标志点的3D深度学习 MICCAI 2015 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-24553-9_69)\n\n## 2016\n### 用于医学图像中解剖标志点检测的人工智能代理 MICCAI 2016 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46726-9_27)\n“深度强化学习”，“解剖标志点检测”\n### 基于级联全卷积神经网络和3D条件随机场的CT肝脏及病灶自动分割 MICCAI 2016 [[论文]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_48)\n“CRF”\n### 基于卷积神经网络的低剂量CT去噪 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.00321)\n### 基于深度神经网络的低剂量CT [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.08508)\n### CT图像中的肺结节检测：利用多视角卷积网络减少假阳性 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7422783\u002F)\n### 利用卷积神经网络和随机视图聚合改进计算机辅助检测 IEEE TMI 2016 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7279156)\n\n## 2017\n### 使用带有Wasserstein距离和感知损失的生成对抗网络进行低剂量CT图像去噪 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00961)\n### 使用对抗性图像到图像网络进行肝脏自动分割 MICCAI 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08037)\n### 使用条件生成对抗网络进行锐度感知的低剂量CT去噪 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.06453)\n### 通过深度卷积框架小波构建U-Net：应用于稀疏视角CT [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.08333)\n### 用于从T1加权MR图像合成CT图像的深度嵌入卷积神经网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02073)\n### 一种自适应采样方案，用于高效训练语义分割的全卷积网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02764)\n### DeepLesion 自动化深度挖掘：基于大规模临床病变标注对重要放射影像发现的分类与检测 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.01766)\n### 用于可变形医学图像配准的无监督端到端学习 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08608)\n### DeepLung 用于自动化肺结节检测与分类的3D深度卷积网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.05538)\n### 使用感知型深度神经网络进行CT图像去噪 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07019)\n### 利用残差卷积网络改善低剂量CT图像 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8082505\u002F)\n### 基于残差编码器-解码器卷积神经网络（RED-CNN）的低剂量CT [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7947200\u002F)\n### 用于低剂量CT降噪的堆叠竞争网络 [[论文]](http:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0190069)\n### 利用3D深度漏斗噪声或网络评估肺结节的恶性程度 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08324)\n### 基于高效3D深度学习的体积数据中稳健的标志点检测 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-42999-1_4)\n### 在不完整3D-CT数据中稳健的多尺度解剖标志点检测 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66182-7_23)\n### 多尺度深度强化学习用于CT扫描中的实时3D标志点检测 TPAMI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8187667\u002F)\n### 用于自动分割体积医学图像的3D深度监督网络 MedIA 2017 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841517300725)\n“深度监督机制”\n### 用于低剂量CT降噪的生成对抗网络 IEEE TMI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7934380)\n\n## 2018\n### 用于全分辨率图像上多类别分割的两阶段3D Unet框架 arXiv 2018[[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04341)\n### DeepLung 用于自动化肺结节检测和分类的深度3D双路径网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09555)\n### Deep LOGISMOS：基于深度学习图论的CT胰腺肿瘤3D分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08599)\n### 注意力U-Net：学习在哪里寻找胰腺 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03999)\n### 基于从2D训练网络迁移学习的低剂量CT用3D卷积编码器-解码器网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.05656)\n### 基于小波残差网络的低剂量CT深度卷积框架小波去噪 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8332971\u002F)\n### 结构敏感的多尺度深度神经网络用于低剂量CT去噪 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00587)\n### 向智能化、鲁棒性的不完整体积数据中解剖结构检测迈进 MedIA 2018 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518304092)\n### 部分策略强化学习用于3D医学图像中的解剖标志点定位 Arxiv 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02908)\n“强化学习”，“解剖标志点定位”，“主动脉瓣”、“左心耳”\n### 深度自监督边缘到轮廓神经网络在肝脏分割中的应用 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00739)\n### 利用循环与形状一致性生成对抗网络进行多模态医学体积的翻译与分割 CVPR 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09655)\n### AnatomyNet 用于快速且全自动全器官解剖分割的深度3D挤压-激励U-Nets 医学物理 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05238)\n### DeepEM 用于弱监督肺结节检测的深度3D ConvNets与EM结合 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05373)\n### 利用多任务3D卷积神经网络计算常染色体显性多囊肾病患者的CT图像总肾体积 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02268)\n### Btrfly Net：基于能量的局部脊柱先验对抗学习进行椎骨标记 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01307)\n### 基于深度学习的肋骨中心线提取与标记 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07082)\n### MICCAI 2018：使用分组单次多框检测器从弱标签多期CT体积中检测肝部病灶 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00934-2_77)\n### CFUN 将Faster R-CNN和U-net网络结合用于高效全心脏分割 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.04914)\n\n### 野外的深层病变图：在多样化的大规模病变数据库中学习和组织重要的放射影像发现 CVPR 2018 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fhtml\u002FYan_Deep_Lesion_Graphs_CVPR_2018_paper.html)\n### 基于CT扫描的3D深度学习预测亚厘米肺腺癌的肿瘤侵袭性 CR 2018 [[论文]](http:\u002F\u002Fcancerres.aacrjournals.org\u002Fcontent\u002F78\u002F24\u002F6881.short)\n### (AH-Net) 3D各向异性混合网络：将卷积特征从2D图像迁移到3D各向异性体素 MICCAI 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00934-2_94)\n“来自计算机断层扫描体积的肝脏及肝肿瘤分割”，“来自数字乳腺断层合成体积的病灶检测”\n### 3D U-JAPA-Net：用于腹部多器官CT分割的卷积网络混合模型 MICCAI 2018 [[论文]]()\n### 用于腹部多器官分割的多尺度3D全卷积网络金字塔 MICCAI 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00937-3_48)\n### 通过双向树LSTM实现冠状动脉自动解剖标注 IJCARS 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11548-018-1884-6)\n\n## 2019\n### 3DFPN-HS2：基于3D特征金字塔网络的高灵敏度和特异性肺结节检测 MICCAI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32226-7_57)\n### 用于冠状动脉CT血管造影中冠状动脉斑块与狭窄自动检测和分类的循环CNN IEEE TMI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8550784)\n### 基于器官注意力网络与统计融合的腹部多器官分割 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518302524)\n### 注意力门控网络：学习利用医学图像中的显著区域 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518306133)\n### 基于深度三维卷积神经网络的冠状动脉粥样硬化自动检测及弱监督定位 冠状动脉CT血管造影 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.13219) [[CMIG论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0895611120300240)\n### 生物医学图像分割深度学习方法的自动化设计 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.08128)\n### 心脏CT血管造影中冠状动脉与左心室心肌的联合分析，用于检测具有功能性显著狭窄的患者 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04940)\n### 使用基于CNN的方向分类器进行心脏CT血管造影中冠状动脉中心线提取 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518308491) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.03143)\n### 基于深度学习和影像组学的CCTA扫描中冠状动脉斑块特征化 MICCAI 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06075)\n### 用于CCTA扫描中冠状动脉斑块特征化的深度学习算法 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06417)\n### 心脏和胸部CT中的直接自动冠状动脉钙化评分 IEEE TMI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8643342)\n### 在心脏CT血管造影中通过3D CNN进行鉴别性冠状动脉追踪 MICCAI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32245-8_52)\n### 利用3D区域建议网络高效定位CT图像中的多个器官 IEEE TMI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8625393)\n### 使用卷积神经网络识别和量化冠状动脉CT血管造影中的运动伪影 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518308624)\n### 使用卷积神经网络对心脏CT血管造影图像进行运动估计与校正 CMIG 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0895611119300515)\n\n## 2020\n### 用于CT中椎体压缩骨折识别的3D卷积序列到序列模型 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03739)\n### 用于通用病变检测的边界图 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09383)\n### C2FNAS：用于3D医学图像分割的粗细结合神经架构搜索 CVPR 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.09628)\n### 融入结构连通性先验的上下文感知细化网络，用于脑部中线勾画 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.05393) [[代码]](https:\u002F\u002Fgithub.com\u002FShawnBIT\u002FBrain-Midline-Detection)\n### CPR-GCN：条件部分残差图卷积网络，在冠状动脉自动解剖标注中使用 CVPR 2020 [[论文]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FYang_CPR-GCN_Conditional_Partial-Residual_Graph_Convolutional_Network_in_Automated_Anatomical_Labeling_CVPR_2020_paper.html)\n### 用于CT扫描中管状结构分割的深度距离变换 CVPR 2020 [[论文]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FWang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html)\n\"\"\n### 基于深度学习的心脏CT血管造影中冠状动脉分析，用于检测需要侵入性冠状动脉造影的患者 IEEE TMI 2020 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8896989)\n### 带有图像先验的深度正弦图补全，用于降低CT图像中的金属伪影 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07469)\n### 边缘门控CNN，用于医学图像的体积分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.04207)\n“纹理和边缘信息”\n### 极端情况下的弱监督医学图像分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.11988)\n### 基于图卷积网络的点云，用于头颈部血管标注 MLMI 2020 [[论文]]()\n### 学习在带有起搏器的心脏CT图像中减少金属伪影 MedIA 2020 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841520300220)\n### 改进型U-Net（mU-Net），融入对象依赖的高级特征，以提升CT图像中的肝脏及肝肿瘤分割 IEEE TMI 2020 [[论文]]()\n### 多分辨率3D卷积神经网络，用于心脏CT血管造影扫描中冠状动脉中心线的自动提取 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00925)\n“改进基于CNN的方向分类器（血管追踪器）”\n### 多视角空间聚合框架，用于头颈部CT图像中危及器官的联合定位与分割 IEEE TMI 2020 [[论文]]()\n### 一键完成CT扫描上的病变RECIST测量与分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.11087)\n\n### PGL 基于先验的局部自监督学习用于三维医学图像分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.12640)\n### RA-UNet 一种混合深度注意力感知网络，用于从CT扫描中提取肝脏和肿瘤 2020\n### 使用3D卷积神经网络快速分割和重建头颈部血管造影图像 NC 2020 [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-18606-2)\n### SenseCare 一个用于医学图像信息学和交互式3D可视化研究平台 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.07031)\n### TopNet 用于血管树重建与标记的拓扑保持度量学习 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08674)\n### TripletUNet 多任务U型网络，结合在线体素级学习，实现精确的CT前列腺分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07462)\n### UXNet 针对3D医学图像分割的多层级特征聚合搜索 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07501)\n\n## 2021\n### 基于可分离卷积神经网络和硬区域加权损失函数的头颈部CT影像中危及器官的自动分割 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01897)\n### CoTr 高效融合CNN与Transformer用于3D医学图像分割 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03024)\n### Swin-Unet 类似Unet的纯Transformer架构用于医学图像分割 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05537)\n### 基于点检测和高斯解纠缠的锥束CT图像牙齿实例分割 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01315)\n\n## 2022\n### 基于Transformer的准确且鲁棒的病灶RECIST直径预测与分割 arXiv 2022 [[论文]]()\n### 面向腹部多器官分割的边界感知网络 arXiv 2022 [[论文]]()\n### 面向肾脏解析的边界感知网络 arXiv 2022 [[论文]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 磁共振成像（MRI）\n## 2022\n### (RefSeg) 在线反射学习用于鲁棒的医学图像分割 MICCAI 2022 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.00476)\n\n## 2015\n### 用于多发性硬化症病灶分割的深度卷积编码器网络 MICCAI 2015 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24574-4_1)\n\n## 2016\n### 多尺度与模态丢弃学习用于椎间盘定位与分割 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-55050-3_8)\n### 使用基于图的决策融合卷积神经网络进行MRI胰腺分割 MICCAI 2016 [[论文]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_51)\n\"CRF\"\n### 使用CNN回归热图进行多处标志点定位 MICCAI 2016 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_27)\n\"多处标志点定位\"\n\n## 2017\n### SegAN 具有多尺度L1损失的对抗网络用于医学图像分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01805)\n### 使用心脏电影MRI图像进行自动分割与疾病分类 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01141)\n### 使用未配对数据进行深度MRI到CT合成 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01155)\n### 用于从MRI和CT中分割心脏亚结构的多平面深度分割网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00983)\n### 用于MRI皮层下区域分割的3D全卷积网络——一项大规模研究 [[论文]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1053811917303324) [[代码]](https:\u002F\u002Fgithub.com\u002Fjosedolz\u002FLiviaNET)\n### 用于心脏MR分割的2D-3D全卷积神经网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09813)\n### 使用密集连接体积卷积网络实现自动3D心血管MR分割\n### 深度生成对抗网络用于压缩感知自动MRI [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00051)\n### 融合纹理与结构的ScatterNet混合深度学习网络（TS-SHDL）用于脑组织分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.09300)\n### 使用级联各向异性卷积神经网络进行自动脑肿瘤分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00382)\n### 域适应深度学习用于加速投影重建MRI [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.01135)\n### 用于动态MRI图像重建的深度卷积神经网络级联 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067520\u002F)\n### 使用生成对抗网络中的循环损失进行压缩感知MRI重建 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00753)\n### 学习变分网络以重建加速MRI数据 [[论文]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmrm.26977)\n### 使用多层感知机的并行MRI成像方法 [[论文]](https:\u002F\u002Faapm.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fmp.12600)\n### 用于动态MRI图像重建的深度卷积神经网络级联 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067520\u002F)\n### 通过域变换流形学习进行图像重建 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.08841)\n### 使用深度全卷积网络实现人类水平的心脏MRI图像分析 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09289)\n### 一种新颖的自动分割方法，用于量化脊髓损伤对人体大腿肌肉和脂肪组织的影响 MICCAI 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66185-8_79)\n\"CRF\"\n### 面向脑肿瘤分割的边界感知全卷积网络 MICCAI 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66185-8_49)\n\"CRF\"\n### 基于上下文感知生成对抗网络的医学图像合成 MICCAI 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-66179-7_48) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05362)\n\n## 2018\n### 使用3D深度密集连接神经网络实现脑部MRI超分辨率 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02728)\n### 具有随机模态体素丢弃学习的3D多尺度FCN，用于从多模态MRI图像中定位和分割椎间盘 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841518300136)\n### 使用生成对抗网络和3D多层级密集连接网络实现高效且准确的MRI超分辨率 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01417)\n### 使用幅度和相位网络进行加速MRI的深度残差学习 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00432)\n### k空间深度学习用于加速MRI [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03779)\n### 探索深度网络中的不确定性度量，用于多发性硬化症病灶的检测和分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01200)\n\n### 基于线索感知深度回归网络的可形变图像配准 TBME 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8331111\u002F)\n### 用于心脏磁共振图像左心室容积估计的多视角融合卷积神经网络 TBME 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8067513\u002F)\n### 针对高度不平衡目标尺寸的指数对数损失3D分割 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00076)\n“焦点损失”、“指数对数损失”\n### 基于上下文感知生成对抗网络的心脏及大血管分割 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-662-56537-7_89)\n### 用于可形变医学图像配准的无监督学习模型 CVPR 2018 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fhtml\u002FBalakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html)\n### VoxelMorph：一种用于可形变医学图像配准的深度学习框架 IEEE TMI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05231)\n### 使用联合运动特征学习架构，无需对比剂直接勾画心肌梗死 MedIA 2018 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518306960)\n### 解剖约束神经网络（ACNN）在心脏影像增强与分割中的应用 IEEE TMI 2018 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051114\u002F)\n### 向仅基于MRI的放疗计划迈进：利用多视角深度卷积神经网络生成合成CT MICCAI 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00928-1_33)\n### 基于结构约束的CycleGAN实现非配对脑部MRI到CT的合成 DLMIA 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-00889-5_20) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04536)\n\n## 2019\n### 用于内存高效体数据分割的半可逆U-Net MICCAI 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.06148) [[代码]](https:\u002F\u002Fgithub.com\u002FRobinBruegger\u002FPartiallyReversibleUnet)\n### 基于双全卷积神经网络的晚钆增强磁共振成像左心房全自动分割 IEEE TMI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8447517)\n\n## 2020\n### 基于图神经网络和层次化精炼的颅内动脉自动标注 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14472)\n### 带有不确定性估计的3D-CNN脑肿瘤分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.12188)\n### CA-Net：用于可解释医学图像分割的综合注意力卷积神经网络 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.10549)\n### （CANet）用于3D脑肿瘤分割的上下文感知网络 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07788)\n### 具有强解剖学保证的心脏分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08825)\n### CS2-Net：医学影像中曲线结构的深度学习分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.07486)\n### 用于引导脑部磁共振图像配准的深度形态简化网络MS-Net PR 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320319304716) [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.02342)\n### 利用额外分类网络提升MRI脑肿瘤分割效果 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.12111)\n### 用于脑肿瘤分割的知识蒸馏 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.03688)\n### MS-Net：用于改善异质MRI数据下前列腺分割的多中心网络 IEEE TMI 2020 [[论文]]()\n### 医学图像分割优化：以Dice分数或Jaccard指数评估时的理论与实践 IEEE TMI 2020 [[论文]]()\n### （AsynDGAN）合成学习：从分布式异步判别器GAN中学习，无需共享医学影像数据 CVPR 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00080)\n“AsynDGAN由一个中央生成器和多个分布在不同医疗机构的判别器组成。”\n### 两阶段级联U-Net：2019年BraTS挑战赛脑部分割任务冠军方案 BrainLes 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-46640-4_22)\n### UNet++：重新设计跳跃连接以挖掘图像分割中的多尺度特征 IEEE TMI 2020 [[论文]]()\n### ψ-Net：堆叠密集卷积LSTM用于皮层下脑结构分割 IEEE TMI 2020 [[论文]]()\n\n## 2021\n### TransBTS：基于Transformer的多模态脑肿瘤分割 arXiv 2021 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04430) [[PyTorch代码]](https:\u002F\u002Fgithub.com\u002FWenxuan-1119\u002FTransBTS)\n\n## 2022\n### 用于3D颈动脉血管壁分割及动脉粥样硬化诊断的标签传播 arXiv 2022 [[论文]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 超声（US）\n## 2015\n### 基于知识迁移循环神经网络的胎儿超声标准切面自动检测 MICCAI 2015 [[论文]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24553-9_62)\n### 通过领域迁移深度神经网络实现胎儿超声标准切面定位 IEEE JBHI 2015 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7090943)\n\n## 2016\n### 基于堆叠深度多项式网络的表示学习，用于小样本超声图像的肿瘤分类 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231216002344)\n### 2016年实时检测与定位胎儿二维自由手超声标准扫描切面 [[论文]]()\n### 2016年使用全卷积神经网络实现实时胎儿超声标准扫描切面的检测与定位 [[论文]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_24)\n### 使用深度卷积神经网络描述超声视频内容 2016年 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493384\u002F)\n\n## 2017\n### 卷积神经网络在医学图像分析中的应用：从头训练还是微调 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00712)\n### 基于空间条件生成对抗网络的自由手超声图像仿真 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05392)\n### 利用对抗学习的深度生成网络模拟病理真实感超声图像 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07881)\n### 解剖约束神经网络（ACNN）在心脏影像增强与分割中的应用 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051114\u002F)\n### Hough-CNN：用于MRI和超声中深层脑区分割的深度学习 CVIU 2017 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1077314217300620)\n\n### 用于自动产前超声图像分割的级联全卷积网络 2017 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950607\u002F)\n### 基于复合神经网络框架的超声标准切面检测 2017 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7890445\u002F)\n### 基于卷积神经网络的超声图像腹围估计 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.02741)\n### 基于超声图像的甲状腺结节自动分割，使用卷积神经网络 IJCARS 2017 [[论文]]\n\"甲状腺\"\n### SonoNet：自由手持超声中胎儿标准扫描切面的实时检测与定位 IEEE TMI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7974824) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05601)\n\n## 2018\n### 基于CNN的剪切波弹性成像乳腺肿瘤分类放射组学方法 IEEE TBME 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8372445\u002F)\n### 面向MR与TRUS图像融合的对抗性图像配准 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.11024)\n### 注意力门控网络用于改进超声扫描切面检测 2018 [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=BJtn7-3sM)\n### 使用随机森林和快速椭圆拟合的超声胎儿头围自动测量 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7927411\u002F)\n### 级联变换式多任务网络用于超声腹部生物特征估计 [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=r1ZGQW2if)\n### 面向超声成像的深度对抗上下文感知关键点检测 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10737)\n### 基于补丁的迭代网络实现快速多关键点定位 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06987) [[TF代码]](https:\u002F\u002Fgithub.com\u002Fyuanwei1989\u002Flandmark-detection)\n### 基于多任务学习的三维胎儿脑部超声全自动对齐至规范参考空间 MedIA 2018 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518300306)\n### 基于形状模型引导的随机森林实现对比超声心动图序列的全自动心肌分割 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8051098\u002F)\n### 基于深度学习的高帧率心脏超声成像 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07823)\n### 基于深度学习的高质量超声多线传输 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07819)\n### 使用全卷积神经网络在胎儿超声中实现人类水平的头部生物特征自动测量 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.09102)\n### 基于更快区域卷积神经网络的磁共振成像中转移性淋巴结识别 CR 2018 [[论文]](http:\u002F\u002Fcancerres.aacrjournals.org\u002Fcontent\u002F78\u002F17\u002F5135.short)\n### 少即是多：腹部超声图像的同时视图分类与关键点检测 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10376)\n### 多任务SonoEyeNet结合生成的超声医师注意力图辅助检测胎儿标准化切面 MICCAI 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-00928-1_98)\n### 基于迭代变换网络的三维胎儿超声标准切面检测 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07486)\n### 胎儿超声图像的弱监督定位 DLMIAW 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00793)\n\n## 2019\n### 自动乳腺超声中基于三维CNN和优先候选聚合的肿瘤检测 IEEE TMI 2018 [[论文]](自动乳腺超声中基于三维CNN和优先候选聚合的肿瘤检测)\n### 基于临床知识指导的卷积神经网络在超声图像中实现甲状腺结节的自动检测与分类 MedIA 2019 [[论文]]()\n\"甲状腺\"\n\n## 2020\n### 对比渲染用于超声图像分割 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.04928)\n### 基于两阶段生成对抗网络的手持超声设备图像质量提升 IEEE TBME 2020 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8698332)\n### 面向冠状动脉内影像血管边界检测的特权模态蒸馏 IEEE TMI 2020 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8896024)\n### 面向医学图像的形状感知半监督三维语义分割 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.10732) [[代码]](https:\u002F\u002Fgithub.com\u002Fkleinzcy\u002FSASSnet)\n### 自监督超声到MRI胎儿脑部图像合成 IEEE TMI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.08698) [[代码]](https:\u002F\u002Fbitbucket.org\u002FJianboJiao\u002Fssus2mri\u002Fsrc)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# X射线\n## 2015\n### 深度学习与结构化预测用于乳房X线片肿块分割 MICCAI 2015 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-24553-9_74)\n\n## 2016\n### 学习阅读胸部X光片：用于自动图像标注的递归神经网络级联模型 2016 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.08486)\n\n## 2017\n### 通过网络级训练卷积神经网络实现精确肺部分割 DLMIA 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00710)\n### 基于深度卷积神经网络的胸部X光片异常检测与定位 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09850)\n### 儿童骨骼年龄评估：使用深度卷积神经网络 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05053)\n\"最近重新实现这一点\", \"用于归一化的分割数据已完成\"\n### 梯度域中用于胸部X光片骨组织抑制的多尺度卷积神经网络级联 2017 [[论文]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841516301529)\n### CheXNet：基于深度学习的胸部X光片肺炎放射科医生级别检测 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05225)\n### 面向乳房X线片肿块分割的对抗性深度结构化网络 MICCAI 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05970)\n### 具有稀疏标签分配的深度多实例网络用于全乳房X线片分类 MICCAI 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-66179-7_69)\n### 用于乳房X线片分类的多尺度CNN与课程学习策略 2017 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-67558-9_20)\n### 高分辨率乳腺癌筛查：使用多视角深度卷积神经网络 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07047)\n### 基于深度学习的未注册多视角乳房X线片自动分析 TMI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8032490\u002F)\n### 深度学习用于X光片中的自动骨骼年龄评估 MedIA 2017\n\"最近重点关注这一点（20181001）\"\n\n## 2018\n\n### 用于胸部X线图像器官分割的扫描结构校正对抗网络 [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJ1RffhjM)\n### 用于胸部X线影像多类别分割的全卷积架构 IEEE TMI 2018 [[TMI论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8302848\u002F) [[ArXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.08816)\n### 用于胸部X线分割无监督域适应的语义感知生成对抗网络 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00600)\n### LF-SegNet：一种用于从胸部X线片中分割肺野的全卷积编码器-解码器网络 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11277-018-5702-9)\n### 使用位置感知密集网络学习识别胸部X线异常 2018 [[论文]]()\n### 深度学习方法在多标签胸部X线分类中的比较 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.02315)\n### 基于深度神经网络的乳腺钼靶图像中肿块分割与形状分类 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01687)\n“条件生成对抗网络”、“INbreast”、“数字筛查乳腺摄影数据库（DDSM）”\n### 基于多任务损失CNN的医学图像描述 2016 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46976-8_13)\n### 用于X线乳腺肿块分割与形状分类的条件生成对抗及卷积网络 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10207)\n### 基于区域生长和CNN分割的良性和恶性乳腺肿瘤分类 ESA 2015 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417414005594)\n### 用于乳腺钼靶图像中肿块分割的对抗式深度结构化网络 ISBI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09288)\n### 使用条件残差U-net改进乳腺钼靶图像中的肿块分割 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.08885)\n### 有限监督下的胸腔疾病识别与定位 CVPR 2018 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers_backup\u002FLi_Thoracic_Disease_Identification_CVPR_2018_paper.pdf)\n### 多分辨率弱监督医学诊断与定位 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07703)\n### 使用卷积神经网络和多实例学习检测数字乳腺断层合成数据中的肿块 CBM 2018 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0010482518300799)\n### 使用ImageNet预训练编码器的U-Net改进胸部X线片中的解剖结构分割 RAMBO 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.02113)\n\n## 2019\n### 使用多视角外推网络进行准确的自动Cobb角估计 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300775)\n### 使用视觉注意力网络学习检测包含肺部病变的胸部X线片 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841518304997)\n### 骨骼抑制与肺野分割何时能提升胸部X线疾病分类 IEEE ISBI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8759510)\n\n## 2020\n### 使用无配对CT结构先验进行高分辨率胸部X线骨骼抑制 IEEE TMI 2020 [[论文]]()\n### 用于胸部X线摄影中多任务器官分割和骨骼抑制的图像到图像转换 IEEE TMI 2020 [[论文]]()\n### 面向脊柱侧弯评估的椎体聚焦地标检测 IEEE ISBI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.03187)\n\n## 2021\n### 冠状动脉疾病的血管造影视频序列自动化深度学习分析 arXiv 2021 [[论文]]()\n### Seg4Reg+：脊柱分割与Cobb角回归之间的一致性学习 MICCAI 2021 [[论文]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n\n# 正电子发射断层扫描（PET）\n## 2017\n### 处理多器官分割中输入输出不平衡的组合损失 arXiv 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02798)\n### 使用深度卷积网络从CT数据生成虚拟PET图像 初步结果 arXiv 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09585)\n\n## 2018\n### 使用卷积神经网络表示进行迭代PET图像重建 IEEE TMI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8463596)\n### 使用深度图像先验进行PET图像重建 IEEE TMI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8581448)\n\n## 2019\n### 使用FCN和GAN网络实现从CT到PET的跨模态合成，以提高自动病灶检测 ENGAPPAI 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0952197618302513)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 眼底镜检查\n## 2016\n### DeepVessel：通过深度学习和条件随机场进行视网膜血管分割 MICCAI 2016 [[论文]](http:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46723-8_16)\n“CRF”\n\n## 2017\n### 使用生成对抗网络对眼底图像进行视网膜血管分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09318) [[Keras+TF代码]](https:\u002F\u002Fbitbucket.org\u002Fwoalsdnd\u002Fv-gan)\n### 向对抗性视网膜图像合成迈进 arXiv 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.08974) [[代码]](https:\u002F\u002Fgithub.com\u002Fcostapt\u002Fvess2ret)\n\n## 2018\n### 端到端对抗性视网膜图像合成 IEEE TMI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8055572) [[代码]](https:\u002F\u002Fgithub.com\u002Fcostapt\u002Fadversarial_retinal_synthesis)\n### 基于多标签深度网络和极坐标变换的眼底视盘与视杯联合分割 TMI 2018 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8252743\u002F)\n### 用于基于深度学习的视网膜血管分割的联合分段级和像素级损失 TBME 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8341481\u002F)\n\n## 2019\n### CE-Net：用于2D医学图像分割的上下文编码器网络 IEEE TMI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8662594)\n### 通过学习图形连通性进行深度血管分割 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300982) [[TF代码]](https:\u002F\u002Fgithub.com\u002Fsyshin1014\u002FVGN)\n\n## 2020\n### 用于基于深度神经网络的眼底图像分割的凸形先验 arXiv 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07476)\n“IVUS图像与眼底图像相似。”\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 显微镜学\n## 2016\n### 使用稀疏自编码器进行染色归一化（StaNoSA）应用于数字病理学 [[论文]](http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0895611116300404)\n\n### 用于乳腺癌组织病理图像中细胞核检测的堆叠稀疏自编码器（SSAE） IEEE TMI 2016 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7163353\u002F)\n\n## 2017\n### 对抗性图像对齐与插值 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00067)\n### 用于肾脏全切片图像分割的CNN级联网络 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00251)\n### 学习分割乳腺活检全切片图像 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02554)\n### SFCN-OPI：利用具有目标先验交互的兄弟FCN进行细胞核检测与细粒度分类 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.08297)\n### MDNet：一种语义和视觉可解释的医学图像诊断网络 CVPR 2017 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FZhang_MDNet_A_Semantically_CVPR_2017_paper.pdf)\n\n## 2018\n### 用于多类别乳腺癌组织病理图像分类的深度学习框架 ICIAR 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00931)\n### 基于神经条件随机场的癌症转移检测 MIDL 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07064)\n### DeepMitosis：通过深度检测、验证和分割网络进行有丝分裂检测 MedIA 2018 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841517301834)\n\n## 2019\n### 用于跨染色组织病理图像分割的双适应金字塔网络 MICCAI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-32245-8_12) [[arXiv论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.11524)\n### HoVer-Net：多组织病理图像中细胞核的同时分割与分类 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519301045)\n### 基于同心损失的弱监督乳腺组织病理图像中有丝分裂检测 MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841519300118)\n\n## 2020\n### 用于重叠宫颈细胞实例分割的深度半监督知识蒸馏 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.10787)\n### 基于星凸多边形的重叠物体MultiStar实例分割 arXiv 2020 [[论文]]()\n### 跨成像实验的细胞核分割——2018年数据科学竞赛 NM 2020 [[论文]]()\n### 基于不平衡数据集的红细胞分割，包含重叠细胞分离与分类 arXiv 2020 [[论文]]()\n\n## 2022\n### 针对组织病理图像中腺体弱监督分割的在线易样本挖掘 MICCAI 2022 [[论文]]()\n### 基于区域引导的CycleGAN用于全切片图像中的染色迁移 arXiv 2022 [[论文]]()\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 结肠镜检查\n## 2016\n### 用于医学图像分析的卷积神经网络：从头训练还是微调？ TMI 2016 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7426826\u002F)\n\n## 2018\n### 基于压缩全卷积网络的结肠镜视频帧实时息肉分割 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-73603-7_32)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# OCT\n## 2017\n### 基于全卷积神经网络和全连接条件随机场的光学相干断层扫描图像中囊样黄斑水肿分割 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.05324)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 皮肤镜检查\n## 2016\n### 基于多尺度病灶偏置表示与联合反向分类的自动黑色素瘤检测 IEEE ISBI 2016 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493447\u002F)\n### 基于深度卷积神经网络和Fisher向量的混合皮肤镜图像分类框架 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950524\u002F)\n### 基于多尺度病灶偏置表示与联合反向分类的自动黑色素瘤检测 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7493447\u002F)\n\n## 2017\n### 基于非常深的残差网络的皮肤镜图像中黑色素瘤自动识别 IEEE TMI 2017 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7792699\u002F)\n### 基于深度全卷积网络并结合Jaccard距离的自动皮肤病变分割 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7903636\u002F)\n“一方面，Jaccard距离类似于已知的Dice重叠系数（也是V-Net中的一种新型损失函数），另一方面，在上述论文中，它是一种适用于二分类分割任务的新颖损失函数。显然，Jaccard距离与IoU（交并比）相似，而IoU是计算机视觉中目标\u002F语义分割任务中的严格度量。”\n### 探究深层侧支网络用于皮肤病变分割 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950514\u002F)\n### 基于Deep RefineNet的皮肤病变分割 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-319-67558-9_35)\n### 利用增强型卷积-反卷积网络改进皮肤镜图像分割 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8239798\u002F)\n### 基于全卷积神经网络的皮肤镜图像分割 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8296578\u002F)\n### 基于全卷积网络的皮肤病变多类别语义分割 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10449)\n“多类别（分类与分割）”\n### 利用增强型卷积-反卷积网络改进皮肤镜图像分割 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8239798\u002F)\n### 基于多阶段全卷积网络的皮肤镜图像分割 [[论文]](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7942129\u002F)\n### 基于循环和卷积神经网络的皮肤黑色素瘤分割 IEEE ISBI 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950522\u002F)\n### 基于混合深度神经网络的皮肤病变分类 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08434)\n### 基于深度神经网络集成的黑色素瘤、痣和脂溢性角化病图像分类 arXiv 2017 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.03108)\n### 基于深度学习的黑色素瘤筛查知识迁移 2017 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7950523\u002F)\n\n## 2018\n### 基于聚合深度卷积特征的皮肤镜图像中黑色素瘤识别 IEEE TBME 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8440053\u002F)\n### 基于区域平均池化的卷积神经网络用于皮肤镜图像分类 IEEE Access 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8502872)\n### 具有特征传递模块的多任务框架用于皮肤病变分类和分割 IEEE ISBI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8363769\u002F)\n\n### 皮肤病变分析用于黑色素瘤检测 IEEE ISBI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8363547\u002F)\n### 用于皮肤病变分割的深度残差架构 ISIC 2018 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-01201-4_30)\n### DermoNet：用于高效皮肤病变分割的密集连接卷积神经网络 [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=B167qcojM)\n### 色素性皮肤病变计算机辅助诊断的技术与算法综述 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1746809417301428)\n### MelanoGANs：基于生成对抗网络的高分辨率皮肤病变合成 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04338)\n### SLSDeep：基于空洞残差和金字塔池化网络的皮肤病变分割 MICCAI 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10241)\n### 基于挤压-激励网络集成与半监督学习的皮肤病变分类 2018 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02568)\n\n## 2019\n### 用于皮肤病变层次化诊断的深度注意力模型 CVPRW 2019 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FBarata_Deep_Attention_Model_for_the_Hierarchical_Diagnosis_of_Skin_Lesions_CVPRW_2019_paper.html)\n### DermaKNet：将皮肤科医生知识融入卷积神经网络以进行皮肤病变诊断 IEEE JBHI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8293766)\n### 全卷积神经网络用于检测临床皮肤镜特征 IEEE JBHI 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8353143)\n### 通过视觉注意力识别黑色素瘤 IPMI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-20351-1_62)\n### 使用带有新型正则化的卷积神经网络进行皮肤病变分类 IEEE Access 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8669763)\n### 单独还是集成：为黑色素瘤分类选择卷积神经网络架构 CVPRW 2019 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FPerez_Solo_or_Ensemble_Choosing_a_CNN_Architecture_for_Melanoma_Classification_CVPRW_2019_paper.html)\n### 借助深度学习实现自动化黑色素瘤检测：数据净化与增强 CVPRW 2019 [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fhtml\u002FISIC\u002FBisla_Towards_Automated_Melanoma_Detection_With_Deep_Learning_Data_Purification_and_CVPRW_2019_paper.html)\n\n## 2020\n### 基于关系驱动自集成模型的半监督医学图像分类 IEEE TMI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07377)\n“该想法可能受到题为‘用于知识蒸馏的相关性一致性 ICCV 2019’的论文启发。”\n### 用于自动化皮肤病变分割与分类的互惠自举模型 IEEE TMI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.03313)\n\n----------------------------------------------------------------------------------------------------------------------------------------\n# 内窥镜检查\n## 2018\n### 基于全卷积网络的铰接式多器械二维姿态估计 IEEE TMI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8259318\u002F) [[代码]](https:\u002F\u002Fgithub.com\u002Fsurgical-vision\u002FEndoVisPoseAnnotation)\n### 机器人微创手术中铰接式器械的三维姿态估计 IEEE TMI 2018 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8295119)\n## 2019\n### 基于内窥镜视频的喉部闭合量化与分析 IEEE TBME 2019 [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8450618)\n### 面向外科视频视觉跟踪的基于补丁的自适应加权与分割及尺度方法（PAWSS） MedIA 2019 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519300593)\n### 在微创手术视频中结合运动流的时序先验进行器械分割 MICCAI 2019 [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-32254-0_49)\n### 2017年机器人器械分割挑战 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.06426)\n### 内窥镜伪影检测（EAD 2019）挑战数据集 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.03209)\n### 用于视频内窥镜质量评估与修复的深度学习框架 arXiv 2019 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07073)\n\n## 2020\n### 从机器人手术视频中学习运动流以实现半监督器械分割 MICCAI 2020 [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.02501) [[代码]](https:\u002F\u002Fgithub.com\u002Fzxzhaoeric\u002FSemi-InstruSeg)\n### 带有相关性损失的多任务循环卷积网络用于外科视频分析 MedIA 2020 [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1361841519301124)","# DeepLearningInMedicalImagingAndMedicalImageAnalysis 快速上手指南\n\n本仓库并非单一的可执行软件工具，而是一个**深度学习在医学影像分析领域的综述论文与数据集索引库**。它汇集了从 2015 年至今关于 CT、MRI、病理切片等模态的分割、检测、配准及去噪等任务的顶级论文（含 arXiv 预印本）和公开数据集链接。\n\n开发者可通过本指南快速定位所需的研究方向、获取基准数据集，并访问对应论文的官方代码实现。\n\n## 环境准备\n\n由于本仓库主要提供文献索引和数据集链接，无需安装特定的运行时环境。但为了复现列表中论文提到的算法，建议准备以下通用深度学习开发环境：\n\n*   **操作系统**: Linux (推荐 Ubuntu 20.04\u002F22.04) 或 macOS\n*   **硬件要求**: 建议使用配备 NVIDIA GPU (显存 ≥ 8GB) 的工作站，以运行大多数 3D 医学影像模型。\n*   **前置依赖**:\n    *   Python 3.8+\n    *   PyTorch 或 TensorFlow (根据具体论文代码要求选择)\n    *   Git (用于克隆仓库和下载代码)\n    *   常用医学影像处理库: `SimpleITK`, `NiBabel`, `OpenCV`, `scikit-image`\n\n## 安装步骤\n\n本仓库本身无需“安装”，只需克隆到本地即可浏览索引。若需运行具体算法，请根据下方“基本使用”中定位到的论文链接，进入其对应的官方代码仓库进行安装。\n\n### 1. 克隆本索引仓库\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FDeepLearningForMedicalImaging\u002FDeepLearningInMedicalImagingAndMedicalImageAnalysis.git\ncd DeepLearningInMedicalImagingAndMedicalImageAnalysis\n```\n\n### 2. 配置国内加速源 (可选)\n在下载具体论文的代码或数据集时，推荐使用国内镜像源加速：\n*   **Git 代码克隆**: 使用 Gitee 镜像（如果作者同步了）或配置 git 代理。\n*   **Python 依赖安装**: 使用清华或阿里源。\n    ```bash\n    pip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n    ```\n*   **PyTorch 安装**: 访问 [PyTorch 国内镜像](https:\u002F\u002Fmirror.tuna.tsinghua.edu.cn\u002Fhelp\u002Fpytorch\u002F) 获取安装命令。\n\n## 基本使用\n\n本仓库的核心用法是**检索 -> 定位 -> 复现**。以下是查找“肾脏肿瘤分割”相关资源并获取代码的最简流程：\n\n### 第一步：检索目标资源\n在克隆后的目录中，使用 `grep` 或直接浏览 `README.md` 查找关键词。例如，查找肾脏（Kidney）相关的论文和数据集：\n\n```bash\n# 搜索包含 \"kidney\" 的行\ngrep -i \"kidney\" README.md\n```\n\n**输出示例定位：**\n> *   **Datasets**: `KiTS 2019` - \"300 Abdomen CT scans for kidney and tumor segmentation\" [[dataset]](https:\u002F\u002Fgithub.com\u002Fneheller\u002Fkits19)\n> *   **Papers**: 可在 CT 或 Segmentation 章节找到相关最新论文（如 2022 ECCV Oral 等）。\n\n### 第二步：获取数据集\n根据索引中的链接，直接访问数据集仓库。以 KiTS19 为例：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fneheller\u002Fkits19.git\n# 注意：大型医学数据集通常需要通过特定脚本下载或申请访问权限，请参考该仓库的具体说明\n```\n\n### 第三步：复现论文算法\n索引中每篇论文标题后通常附有 `[[code]]` 链接。假设你找到了某篇关于肾脏分割的论文代码链接（例如 `https:\u002F\u002Fgithub.com\u002FTopoXLab\u002FTopoInteraction`）：\n\n1.  **克隆代码仓库**:\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002FTopoXLab\u002FTopoInteraction.git\n    cd TopoInteraction\n    ```\n\n2.  **创建虚拟环境并安装依赖**:\n    ```bash\n    python -m venv venv\n    source venv\u002Fbin\u002Factivate  # Windows 使用: venv\\Scripts\\activate\n    pip install -r requirements.txt\n    ```\n\n3.  **运行示例推理**:\n    (具体命令需参照该具体代码仓库的 README，以下为通用示例)\n    ```bash\n    python infer.py --data_path ..\u002Fkits19\u002Fdata --checkpoint pretrained_model.pth\n    ```\n\n### 常用资源分类速查\n*   **综述论文 (Survey\u002FReview)**: 查看 README 开头的 `Review and Survey` 部分，适合入门了解技术演进。\n*   **数据集 (Datasets)**: 查看 `Datasets` 部分，涵盖胸部 X 光 (JSRT, ChestX-ray8)、腹部 CT (KiTS)、皮肤镜 (ISIC) 等。\n*   **按模态查找**: 向下滚动至 `Computed Tomography (CT)`、`MRI` 等章节，按年份查找最新 SOTA 方法。","某三甲医院放射科团队正致力于研发一套基于深度学习的肺结节自动检测系统，但在算法选型和架构设计阶段面临巨大的文献调研压力。\n\n### 没有 DeepLearningInMedicalImagingAndMedicalImageAnalysis 时\n- **文献检索如大海捞针**：研究人员需在 IEEE、Springer、arXiv 等数十个数据库中手动搜索，耗时数周仍难以覆盖 2016 至 2020 年间关于医学图像分割与配准的关键论文。\n- **技术路线决策盲目**：由于缺乏对生成对抗网络（GANs）在数据增强中应用的系统性综述，团队不敢轻易尝试小样本下的模型优化，导致初期实验效果不佳。\n- **重复造轮子现象严重**：因未及时掌握多模态融合的最新进展，团队花费大量时间复现了已被证明效率低下的旧式单模态处理流程。\n- **领域知识结合困难**：开发人员难以快速找到将临床先验知识（如解剖结构约束）融入深度学习模型的具体案例，导致算法在实际病灶识别中误报率偏高。\n\n### 使用 DeepLearningInMedicalImagingAndMedicalImageAnalysis 后\n- **一站式获取权威综述**：团队直接利用该资源库中整理的 20 余篇核心综述（涵盖从基础分割到 COVID-19 诊断），在两天内就完成了过去需一个月的技术背景调研。\n- **精准锁定前沿方案**：通过查阅其中关于 GANs 和无完美数据集处理的专项回顾，团队迅速采用了成熟的数据合成策略，显著提升了小样本下的模型鲁棒性。\n- **规避过时技术陷阱**：参考多模态融合的最新调查，团队直接跳过了单模态方案的试错，构建了更高效的 CT 与 PET 影像联合分析架构。\n- **加速临床落地转化**：借助领域知识驱动的深度学习案例指导，团队成功将解剖学约束嵌入损失函数，使肺结节检测的假阳性率降低了 40%。\n\nDeepLearningInMedicalImagingAndMedicalImageAnalysis 通过将分散的学术成果系统化，成为医疗 AI 研发团队从理论探索走向高效落地的关键导航图。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fshawnyuen_DeepLearningInMedicalImagingAndMedicalImageAnalysis_7315525c.png","shawnyuen","Shaofeng Yuan","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fshawnyuen_4178fe7c.jpg","too young, too simple, sometimes naive","Dongguan, China","yuanshaofengsmu@qq.com","https:\u002F\u002Fscholar.google.com\u002Fcitations?user=ZF3aeCAAAAAJ","https:\u002F\u002Fgithub.com\u002Fshawnyuen",531,127,"2026-03-24T07:17:33",1,"","未说明",{"notes":88,"python":86,"dependencies":89},"该仓库并非一个可执行的软件工具或代码库，而是一个医学影像深度学习领域的论文综述（Review and Survey）及相关数据集链接的汇总列表。README 内容主要包含历年学术论文标题、链接以及公开数据集介绍，不包含任何源代码、安装脚本或具体的运行环境配置要求。用户需根据列表中具体引用的论文或其对应的独立代码仓库去查询特定的环境需求。",[],[15,91],"其他","2026-03-27T02:49:30.150509","2026-04-11T03:24:23.843923",[],[]]