Domain adaptation and representation transfer : 5th MICCAI Workshop, DART 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. Domain Adaptation and Representation Transfer (Workshop) (5th : ...最新文献
Mohammad Reza Hosseinzadeh Taher, Michael B Gotway, Jianming Liang
{"title":"Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-supervision.","authors":"Mohammad Reza Hosseinzadeh Taher, Michael B Gotway, Jianming Liang","doi":"10.1007/978-3-031-45857-6_10","DOIUrl":"10.1007/978-3-031-45857-6_10","url":null,"abstract":"<p><p>Human anatomy is the foundation of medical imaging and boasts one striking characteristic: its hierarchy in nature, exhibiting two intrinsic properties: (1) <i>locality</i>: each anatomical structure is morphologically distinct from the others; and (2) <i>compositionality</i>: each anatomical structure is an integrated part of a larger whole. We envision a foundation model for medical imaging that is <i>consciously</i> and <i>purposefully</i> developed upon this foundation to gain the capability of \"understanding\" human anatomy and to possess the fundamental properties of medical imaging. As our first step in realizing this vision towards foundation models in medical imaging, we devise a novel self-supervised learning (SSL) strategy that exploits the hierarchical nature of human anatomy. Our extensive experiments demonstrate that the SSL pretrained model, derived from our training strategy, not only outperforms state-of-the-art (SOTA) fully/self-supervised baselines but also enhances annotation efficiency, offering potential few-shot segmentation capabilities with performance improvements ranging from 9% to 30% for segmentation tasks compared to SSL baselines. This performance is attributed to the significance of <i>anatomy comprehension</i> via our learning strategy, which encapsulates the intrinsic attributes of anatomical structures-<i>locality</i> and <i>compositionality</i>-within the embedding space, yet overlooked in existing SSL methods. All code and pretrained models are available at GitHub.com/JLiangLab/Eden.</p>","PeriodicalId":519912,"journal":{"name":"Domain adaptation and representation transfer : 5th MICCAI Workshop, DART 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. Domain Adaptation and Representation Transfer (Workshop) (5th : ...","volume":"14293 ","pages":"94-104"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11095552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}