{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:48:30Z","timestamp":1776070110491,"version":"3.50.1"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council (EPSRC) Programme","doi-asserted-by":"publisher","award":["EP\/P001009\/1"],"award-info":[{"award-number":["EP\/P001009\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/W01842X\/1"],"award-info":[{"award-number":["EP\/W01842X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006041","name":"Innovate U.K","doi-asserted-by":"publisher","award":["104691"],"award-info":[{"award-number":["104691"]}],"id":[{"id":"10.13039\/501100006041","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["884622"],"award-info":[{"award-number":["884622"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1109\/tmi.2022.3224067","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T19:11:42Z","timestamp":1669317102000},"page":"1095-1106","source":"Crossref","is-referenced-by-count":201,"title":["Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3069-8708","authenticated-orcid":false,"given":"Cheng","family":"Ouyang","sequence":"first","affiliation":[{"name":"Department of Computing, Imperial College London, London, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-9755","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, U.K"}]},{"given":"Surui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-2959","authenticated-orcid":false,"given":"Zeju","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-3092","authenticated-orcid":false,"given":"Chen","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering and Imperial-X, Imperial College London, London, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2943-7698","authenticated-orcid":false,"given":"Wenjia","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Computing, Department of Brain Sciences, Imperial College London, London, U.K"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, U.K"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref3","article-title":"nnU-Net: Self-adapting framework for U-Net-based medical image segmentation","author":"Isensee","year":"2018","journal-title":"arXiv:1809.10486"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2973595"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_47"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/96"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref10","article-title":"Machine learning with multi-site imaging data: An empirical study on the impact of scanner effects","author":"Glocker","year":"2019","journal-title":"arXiv:1910.04597"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17478-w"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"Pearl","year":"2009"},{"key":"ref15","article-title":"Causal intervention for weakly-supervised semantic segmentation","author":"Zhang","year":"2020","journal-title":"arXiv:2009.12547"},{"key":"ref16","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Ganin"},{"key":"ref17","first-page":"10","article-title":"Domain generalization via invariant feature representation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Muandet"},{"key":"ref18","first-page":"4555","article-title":"Selecting data augmentation for simulating interventions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ilse"},{"key":"ref19","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"Tzeng","year":"2014","journal-title":"arXiv:1412.3474"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_74"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101907"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102617"},{"key":"ref24","first-page":"6450","article-title":"Domain generalization via model-agnostic learning of semantic features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Dou"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00153"},{"key":"ref26","article-title":"Domain generalization with MixStyle","author":"Zhou","year":"2021","journal-title":"arXiv:2104.02008"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2764326"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_18"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_46"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_29"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_23"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433930"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref36","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"25","author":"Krizhevsky"},{"key":"ref37","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref38","article-title":"Generalizing to unseen domains via adversarial data augmentation","author":"Volpi","year":"2018","journal-title":"arXiv:1805.12018"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"ref40","article-title":"Robust and generalizable visual representation learning via random convolutions","author":"Xu","year":"2020","journal-title":"arXiv:2007.13003"},{"key":"ref41","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018","journal-title":"arXiv:1808.06670"},{"key":"ref42","article-title":"Supervised contrastive learning","author":"Khosla","year":"2020","journal-title":"arXiv:2004.11362"},{"key":"ref43","first-page":"10410","article-title":"On the generalization effects of linear transformations in data augmentation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wu"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref45","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Verma"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00029"},{"key":"ref49","article-title":"A learning strategy for contrast-agnostic MRI segmentation","author":"Billot","year":"2020","journal-title":"arXiv:2003.01995"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_65"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_14"},{"key":"ref52","article-title":"Invariant risk minimization","author":"Arjovsky","year":"2019","journal-title":"arXiv:1907.02893"},{"key":"ref53","first-page":"7313","article-title":"Domain generalization using causal matching","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Mahajan"},{"key":"ref54","article-title":"A causal view of compositional zero-shot recognition","author":"Atzmon","year":"2020","journal-title":"arXiv:2006.14610"},{"key":"ref55","article-title":"Representation learning via invariant causal mechanisms","author":"Mitrovic","year":"2020","journal-title":"arXiv:2010.07922"},{"key":"ref56","first-page":"1","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Sajjadi"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2546221"},{"key":"ref58","article-title":"Grid saliency for context explanations of semantic segmentation","author":"Hoyer","year":"2019","journal-title":"arXiv:1907.13054"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00255"},{"key":"ref60","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv:1804.03999"},{"key":"ref61","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-6333-3","volume-title":"A Practical Guide to Splines","volume":"27","author":"De Boor","year":"1978"},{"key":"ref62","article-title":"AugMix: A simple data processing method to improve robustness and uncertainty","author":"Hendrycks","year":"2019","journal-title":"arXiv:1912.02781"},{"key":"ref63","article-title":"AirLab: Autograd image registration laboratory","author":"Sandk\u00fchler","year":"2018","journal-title":"arXiv:1806.09907"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.7303\/SYN3193805"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101950"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102528"},{"issue":"6","key":"ref67","first-page":"1","article-title":"NCI-ISBI 2013 challenge: Automated segmentation of prostate structures","volume":"370","author":"Bloch","year":"2015","journal-title":"Cancer Imag. Arch."},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.02.009"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.12.002"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"ref71","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Tan"},{"key":"ref72","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000022288.19776.77"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10091712\/09961940.pdf?arnumber=9961940","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:52:33Z","timestamp":1706755953000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9961940\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":73,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2022.3224067","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4]]}}}