{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:00:53Z","timestamp":1777888853532,"version":"3.51.4"},"reference-count":70,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2112562"],"award-info":[{"award-number":["2112562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007431","name":"NRF","doi-asserted-by":"publisher","award":["RS-2023-00251366"],"award-info":[{"award-number":["RS-2023-00251366"]}],"id":[{"id":"10.13039\/100007431","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.02074","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"22338-22348","source":"Crossref","is-referenced-by-count":1,"title":["Progressive Test Time Energy Adaptation for Medical Image Segmentation"],"prefix":"10.1109","author":[{"given":"Xiaoran","family":"Zhang","sequence":"first","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byung-Woo","family":"Hong","sequence":"additional","affiliation":[{"name":"Chung-Ang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoungseob","family":"Park","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel H.","family":"Pak","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne-Marie","family":"Rickmann","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lawrence H.","family":"Staib","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James S.","family":"Duncan","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex","family":"Wong","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"3080","article-title":"MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics","author":"Bartler","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3090082"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51701.2025.00567"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00150"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00511"},{"key":"ref8","author":"Du","year":"2020","journal-title":"Implicit Generation and Generalization in Energy-Based Models"},{"key":"ref9","first-page":"877","article-title":"A brief review of domain adaptation. Advances in data science and information engineering","volume-title":"proceedings from ICDATA 2020 and IKE 2020","author":"Farahani","year":"2021"},{"key":"ref10","first-page":"1180","article-title":"Unsupervised Domain Adaptation by Backpropagation","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","author":"Ganin","year":"2015"},{"key":"ref11","author":"Goodfellow","year":"2015","journal-title":"Explaining and Harnessing Adversarial Examples"},{"key":"ref12","author":"Grathwohl","year":"2020","journal-title":"Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One"},{"key":"ref13","author":"Gu","year":"2024","journal-title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3117407"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00887"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref17","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume-title":"Advances in Neural Information Processing Systems","author":"Ho","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_24"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72114-4_47"},{"issue":"6","key":"ref21","first-page":"475","article-title":"Two public chest xray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"Jaeger","year":"2014","journal-title":"Quantitative imaging in medicine and surgery"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3110179"},{"key":"ref23","author":"Kingma","year":"2017","journal-title":"A Method for Stochastic Optimization"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3005297"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2900516"},{"key":"ref26","author":"LeCun","journal-title":"A Tutorial on Energy-Based Learning"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3370978"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102808"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"ref30","first-page":"21808","article-title":"TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive","volume-title":"Advances in Neural Information Processing Systems","author":"Liu","year":"2021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-025-01608-0"},{"key":"ref34","author":"Jun","year":"2024","journal-title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622112"},{"key":"ref36","first-page":"8162","article-title":"Improved Denoising Diffusion Probabilistic Models","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Quinn Nichol","year":"2021"},{"key":"ref37","first-page":"16888","article-title":"Efficient TestTime Model Adaptation without Forgetting","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Niu","year":"2022"},{"key":"ref38","author":"Niu","year":"2023","journal-title":"TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01939"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00149"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.026"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.03.010"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"ref45","author":"Schneider","year":"2020","journal-title":"Improving robustness against common corruptions by covariate shift adaptation"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.174.1.1740071"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3237025"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01147"},{"key":"ref49","article-title":"Pixeldefend: Leveraging generative models to understand and defend against adversarial examples","author":"Song","year":"2017","journal-title":"arXiv preprint"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2024.1359317"},{"key":"ref51","author":"Sun","year":"2019","journal-title":"Test-Time Training for Out-of Distribution Generalization"},{"key":"ref52","first-page":"9229","article-title":"Test-Time Training with SelfSupervision for Generalization under Distribution Shifts","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Sun","year":"2020"},{"key":"ref53","author":"Maria","year":"2022","journal-title":"On-the-Fly Test-time Adaptation for Medical Image Segmentation"},{"key":"ref54","article-title":"Attention is All you Need","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems. Curran Associates, Inc."},{"key":"ref55","author":"Wang","year":"2020","journal-title":"Tent: Fully Test-Time Adaptation by Entropy Minimization"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"ref57","author":"Wang","year":"2024","journal-title":"Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation"},{"key":"ref58","author":"Weihsbach","year":"2024","journal-title":"DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00052"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3400066"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.09.005"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102457"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02256"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24850"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2799"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621570"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104345"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101916"},{"key":"ref69","article-title":"Towards scalable languageimage pre-training for 3d medical imaging","author":"Zhao","year":"2025","journal-title":"arXiv preprint"},{"key":"ref70","first-page":"914","article-title":"Bayesian adaptation for covariate shift","volume":"34","author":"Zhou","year":"2021","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11444875.pdf?arnumber=11444875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:00:13Z","timestamp":1777611613000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11444875\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":70,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.02074","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}