本汇编收集了2020年7月6日至9日在加拿大蒙特利尔举行的第三届医学成像与深度学习国际会议（MIDL 2020）的所有已接受的扩展摘要。请注意，这里只列出了可接受的扩展摘要，MIDL 2020 Full Paper Track的论文集发表在Proceedings of机器学习研究（PMLR）中。
英文摘要：
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).
When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis
Macquarie University（麦考瑞大学）；Macquarie Medical School（麦考瑞医学院）；Faculty of Medicine, Health and Human Sciences（医学与健康科学学院）；School of Computing（计算机学院）；Faculty of Science and Engineering（科学与工程学院）；Centre for Health Informatics（健康信息学中心）；Australian Institute of Health Innovation（澳大利亚健康创新研究院）
EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging
Radboud University Medical Center（拉德堡德大学医学中心）；Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands（荷兰奈梅亨拉德堡德大学医学中心诊断图像分析组）
Markerless Tracking-Based Registration for Medical Image Motion Correction
Department Artificial Intelligence in Biomedical Engineering, FAU Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, East Carolina University（通讯科学与障碍系，东卡罗来纳大学）；Department of Otolaryngology–Head and Neck Surgery, Louisiana State University Health Sciences Center（耳鼻喉科与头颈外科系，路易斯安那州立大学健康科学中心）；Department of Radiology, FAU Erlangen-Nürnberg（放射学系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, Louisiana State University（通讯科学与障碍系，路易斯安那州立大学）
Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Visual Search Patterns in 3D Pancreatic Imaging: An Eye Tracking Study
Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Department of Radiology, Herlev Hospital（赫尔勒夫医院放射科）；Department of Radiology, University of Iowa（爱荷华大学放射科）
A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage
Siemens Healthineers（西门子医疗）；New York University（纽约大学）；University of Maryland Medical Center（马里兰大学医学中心）；Vancouver General Hospital（温哥华总医院）；Northwell Health（诺斯韦尔健康）；UCHealth Memorial Hospital（UCHealth纪念医院）；Siemens SRL（西门子罗马尼亚有限责任公司）；Transilvania University of Braşov（布拉索夫特兰西瓦尼亚大学）
CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI
Microsoft Health and Life Sciences（微软健康与生命科学部）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与成像人工智能中心）
临床人工智能（AI）应用正在全球范围内迅速扩展，并有可能影响到医疗实践的所有领域。医学成像应用占批准的临床AI应用的绝大多数。尽管医疗系统渴望采用人工智能解决方案，但仍然存在一个基本问题：\textit{what happens after the AI model goes into production?}我们使用CheXpert和PadChest公共数据集构建和测试医疗成像人工智能漂移监测工作流程，以跟踪数据和模型漂移，而无需同时获得地面实况。我们在多个实验中模拟漂移，将模型性能与我们新的多模态漂移度量进行比较，该度量使用DICOM元数据，变分自动编码器（VAE）的图像外观表示和模型输出概率作为输入。通过实验，我们展示了一个强大的代理地面真相的性能，使用相关元数据，预测概率和VAE潜在表示的无监督分布变化。我们的主要贡献包括（1）医学成像漂移检测的概念验证，包括使用VAE和特定领域的统计方法，（2）测量和统一漂移指标的多模式方法，（3）对观察部署的医学成像AI的挑战和解决方案的新见解，以及（4）创建开源工具，使其他人能够轻松运行自己的工作流程和场景。这项工作具有重要意义。它解决了在动态医疗环境中常见的连续医学成像AI模型监测中发现的相关翻译差距。
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）
DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis
Recent Advances in Medical Imaging Segmentation: A Survey
Junia, UMR 8520, CNRS, Centrale Lille, Univerity of Polytechnique Hauts-de-France（Junia研究院、法国国家科学研究中心、Centrale Lille、法国高等技术大学 Hauts-de-France）；Sorbonne Center for Artificial Intelligence (SCAI), Abu Dhabi, UAE（索邦人工智能中心（SCAI））
MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging
1 School of Computation, Information；Technology, Technical University of Munich 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich 3 Munich Center for Machine Learning (MCML) 4 Institute for Diagnostic；Interventional Radiology, Klinkum Rechts der Isar 5 School of Biomedical Engineering \& Imaging Sciences, King’s College London
An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies
Germany Cancer Research Center（德国癌症研究中心）；University of Heidelberg（海德堡大学）；University Hospital Frankfurt（法兰克福大学医院）；Charite Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health（柏林健康研究所）
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
SUNY Buffalo（纽约州立大学布法罗分校）；Medical College of Wisconsin（威斯康星医学院）；Washington University School of Medicine（华盛顿大学医学院）；University of California, Davis Medical Center（加州大学戴维斯医学中心）
An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing
Dept. of Electrical and Computer Engineering, Vanderbilt University（电气与计算机工程系，范德比尔特大学）；Dept. of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Dept. of Biomedical Engineering, Vanderbilt University（生物医学工程系，范德比尔特大学）；Dept. of Surgery, Vanderbilt University Medical Center（外科系，范德比尔特大学医学中心）；Dept. of Neurology, Vanderbilt University Medical Center（神经病学系，范德比尔特大学医学中心）；Dept. of Medicine, Vanderbilt University Medical Center（医学系，范德比尔特大学医学中心）；Dept. of Thoracic Surgery, Vanderbilt University Medical Center（胸外科系，范德比尔特大学医学中心）；Dept. of Biomedical Informatics, Vanderbilt University Medical Center（生物医学信息学系，范德比尔特大学医学中心）；Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center（放射学与放射科学系，范德比尔特大学医学中心）；Vanderbilt University Institute of Imaging Science（范德比尔特大学成像科学研究所）；Veteran Affairs, Tennessee Valley Healthcare System（退伍军人事务，田纳西河流域医疗系统）；Peabody College, Vanderbilt University（佩博利学院，范德比尔特大学）；Vanderbilt Brain Institute, Vanderbilt University（范德比尔特脑研究院，范德比尔特大学）
Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（集成空天地海大数据应用技术国家工程实验室）；School of Computer Science and Engineering, Northwestern Polytechnical University（计算机科学与工程学院，西北工业大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）
