{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:02:32Z","timestamp":1777654952493,"version":"3.51.4"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000780","name":"ERC under the EU's Horizon 2020 Research and Innovation Program","doi-asserted-by":"publisher","award":["757173"],"award-info":[{"award-number":["757173"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council (CSC) Imperial Scholarship","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1109\/tmi.2023.3282728","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T17:53:07Z","timestamp":1685987587000},"page":"3323-3335","source":"Crossref","is-referenced-by-count":6,"title":["Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-2959","authenticated-orcid":false,"given":"Zeju","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computing, BioMedIA Group, Imperial College London, London, U.K"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3281-6509","authenticated-orcid":false,"given":"Konstantinos","family":"Kamnitsas","sequence":"additional","affiliation":[{"name":"Department of Computing, BioMedIA Group, Imperial College London, London, U.K"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-9950","authenticated-orcid":false,"given":"Qi","family":"Dou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-3092","authenticated-orcid":false,"given":"Chen","family":"Qin","sequence":"additional","affiliation":[{"name":"I-X and the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-9356","authenticated-orcid":false,"given":"Ben","family":"Glocker","sequence":"additional","affiliation":[{"name":"Department of Computing, BioMedIA Group, Imperial College London, London, U.K"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Generative image translation for data augmentation of bone lesion pathology","author":"gupta","year":"2019","journal-title":"arXiv 1902 02248"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.103"},{"key":"ref12","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv 1412 6572"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1198\/10618600152418584"},{"key":"ref15","article-title":"The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes","author":"heller","year":"2019","journal-title":"arXiv 1904 00445"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_1"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102136"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_37"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_40"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_46"},{"key":"ref11","first-page":"1568","article-title":"Bilevel programming for hyperparameter optimization and meta-learning","author":"franceschi","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref33","article-title":"Better aggregation in test-time augmentation","author":"shanmugam","year":"2020","journal-title":"arXiv 2011 11156"},{"key":"ref10","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume":"70","author":"finn","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref32","first-page":"737","article-title":"Hyperparameter optimization with approximate gradient","author":"pedregosa","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref2","first-page":"1","article-title":"NCI-ISBI 2013challenge: Automated segmentation of prostate structures","volume":"370","author":"bloch","year":"2015","journal-title":"Cancer Imag Arch"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015187"},{"key":"ref17","article-title":"Categorical reparameterization with Gumbel–Softmax","author":"jang","year":"2016","journal-title":"arXiv 1611 01144"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00874"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref38","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101907"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"ref24","first-page":"6765","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"li","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.02.009"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3046692"},{"key":"ref25","article-title":"DADA: Differentiable automatic data augmentation","author":"li","year":"2020","journal-title":"arXiv 2003 03780"},{"key":"ref20","article-title":"Learning loss for test-time augmentation","author":"kim","year":"2020","journal-title":"arXiv 2010 11422"},{"key":"ref22","author":"landman","year":"2020","journal-title":"2015 MICCAI Multi-Atlas Labeling Beyond the Cranial Vault Workshop and Challenge"},{"key":"ref21","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref28","article-title":"Fast AutoAugment","author":"lim","year":"2019","journal-title":"arXiv 1905 00397"},{"key":"ref27","article-title":"A large, open source dataset of stroke anatomical brain images and manual lesion segmentations","volume":"5","author":"liew","year":"2018","journal-title":"Data Science Journal"},{"key":"ref29","article-title":"DARTS: Differentiable architecture search","author":"liu","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref8","article-title":"RandAugment: Practical automated data augmentation with a reduced search space","author":"cubuk","year":"2019","journal-title":"arXiv 1909 13719"},{"key":"ref7","article-title":"AutoAugment: Learning augmentation policies from data","author":"cubuk","year":"2018","journal-title":"arXiv 1805 09501"},{"key":"ref9","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017","journal-title":"arXiv 1708 04552"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_65"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101934"},{"key":"ref6","first-page":"411","article-title":"Mitosis detection in breast cancer histology images with deep neural networks","author":"cire?an","year":"2013","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref5","first-page":"424","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"abdulkadir","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref40","article-title":"Neural architecture search with reinforcement learning","author":"zoph","year":"2017","journal-title":"Proc Int Conf Learn Represent"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10299534\/10144095.pdf?arnumber=10144095","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:20:08Z","timestamp":1699903208000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10144095\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":40,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2023.3282728","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11]]}}}