{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T01:39:46Z","timestamp":1774575586781,"version":"3.50.1"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,18]]},"DOI":"10.1109\/isbi53787.2023.10230401","type":"proceedings-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T17:23:07Z","timestamp":1693588987000},"page":"1-5","source":"Crossref","is-referenced-by-count":3,"title":["How Precise are Performance Estimates for Typical Medical Image Segmentation Tasks?"],"prefix":"10.1109","author":[{"given":"Rosana","family":"El Jurdi","sequence":"first","affiliation":[{"name":"Sorbonne Université,Institut Du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de La Pitié Salpêtrière,Paris,France,F-75013"}]},{"given":"Olivier","family":"Colliot","sequence":"additional","affiliation":[{"name":"Sorbonne Université,Institut Du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de La Pitié Salpêtrière,Paris,France,F-75013"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3150005"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3105046"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3117996"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref10","article-title":"A surprisingly effective perimeter-based loss for medical image segmentation","author":"jurdi","year":"2021","journal-title":"Medical Imaging with Deep Learning (MIDL)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.06.061"},{"key":"ref1","first-page":"1089","article-title":"No unbiased estimator of the variance of K-fold cross-validation","volume":"5","author":"bengio","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3186731"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3162111"},{"key":"ref19","first-page":"1","article-title":"CAT-Net: A cross-slice attention transformer model for prostate zonal segmentation in MRI","author":"sung","year":"0","journal-title":"IEEE TMI"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3132291"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3175461"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3130469"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3104460"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3149168"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3114097"},{"key":"ref22","first-page":"1","article-title":"Semi-supervised unpaired medical image segmentation through task-affinity consistency","author":"chen","year":"2022","journal-title":"IEEE TMI"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3128408"},{"key":"ref27","author":"simpson","year":"2019","journal-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2802944"},{"key":"ref7","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"MICCAI"},{"key":"ref9","first-page":"285","article-title":"Boundary loss for highly unbalanced segmentation","volume":"102","author":"kervadec","year":"2019","journal-title":"Medical Imaging with Deep Learning (MIDL)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.332.7549.1080"},{"key":"ref3","article-title":"Evaluating machine learning models and their diagnostic value","volume":"hal 3682454","author":"varoquaux","year":"2022","journal-title":"Preprint Hal"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"}],"event":{"name":"2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)","location":"Cartagena, Colombia","start":{"date-parts":[[2023,4,18]]},"end":{"date-parts":[[2023,4,21]]}},"container-title":["2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10230311\/10230322\/10230401.pdf?arnumber=10230401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T17:54:34Z","timestamp":1695664474000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10230401\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/isbi53787.2023.10230401","relation":{},"subject":[],"published":{"date-parts":[[2023,4,18]]}}}