{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:53:31Z","timestamp":1789106011168,"version":"build-2803163510"},"reference-count":27,"publisher":"Radiological Society of North America (RSNA)","issue":"1","funder":[{"DOI":"10.13039\/501100001659","name":"DFG","doi-asserted-by":"publisher","award":["504320104"],"award-info":[{"award-number":["504320104"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Radiology: Artificial Intelligence"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>Domain shift hindered clinical translation of deep learning segmentation models in medical imaging; random convolutions improved segmentation generalization by compelling models to prioritize shape over texture features.<\/jats:p>","DOI":"10.1148\/ryai.240502","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T14:52:48Z","timestamp":1763563968000},"source":"Crossref","is-referenced-by-count":3,"title":["Random Convolutions for Domain Generalization of Deep                     Learning\u2013based Medical Image Segmentation Models"],"prefix":"10.1148","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0789-8669","authenticated-orcid":false,"given":"Daniel","family":"Scholz","sequence":"first","affiliation":[{"name":"Institute for Diagnostic and Interventional                     Neuroradiology, Klinikum rechts der Isar, Technical University of Munich,                     Neuro-Kopf-Zentrum, Ismaninger Str 22, 81675 Munich, Germany"},{"name":"Institute for Artificial Intelligence and Informatics in                     Medicine, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0290-0434","authenticated-orcid":false,"given":"Ayhan Can","family":"Erdur","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence and Informatics in                     Medicine, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-9853","authenticated-orcid":false,"given":"Jan C.","family":"Peeken","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Klinikum rechts der                     Isar, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5804-7343","authenticated-orcid":false,"given":"Aswathi","family":"Varma","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional                     Neuroradiology, Klinikum rechts der Isar, Technical University of Munich,                     Neuro-Kopf-Zentrum, Ismaninger Str 22, 81675 Munich, Germany"},{"name":"Institute for Artificial Intelligence and Informatics in                     Medicine, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-3680","authenticated-orcid":false,"given":"Robert","family":"Graf","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional                     Neuroradiology, Klinikum rechts der Isar, Technical University of Munich,                     Neuro-Kopf-Zentrum, Ismaninger Str 22, 81675 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-0003","authenticated-orcid":false,"given":"Jan S.","family":"Kirschke","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional                     Neuroradiology, Klinikum rechts der Isar, Technical University of Munich,                     Neuro-Kopf-Zentrum, Ismaninger Str 22, 81675 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence and Informatics in                     Medicine, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2963-7772","authenticated-orcid":false,"given":"Benedikt","family":"Wiestler","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic and Interventional                     Neuroradiology, Klinikum rechts der Isar, Technical University of Munich,                     Neuro-Kopf-Zentrum, Ismaninger Str 22, 81675 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1577","reference":[{"key":"r1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"r3","unstructured":"Guo B, Lu D, Szumel G, et                             al. The Impact of Scanner                         Domain Shift on Deep Learning Performance in Medical Imaging: an                         Experimental Study. arXiv 2024. Preprint                         posted online September 6, 2024; doi:10.48550\/arXiv.2409.04368."},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107580"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM55620.2022.9994999"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2023.08.003"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43901-8_9"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3564765"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2024.3507831"},{"key":"r10","unstructured":"Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W. ImageNet-trained                         CNNs are biased towards texture; increasing shape bias improves accuracy and                         robustness. arXiv 2018. Preprint posted                         online November 29, 2018; doi:10.48550\/arxiv.1811.12231."},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00994"},{"key":"r12","unstructured":"Xu Z, Liu D, Yang J, Raffel C, Niethammer M. Robust                         and Generalizable Visual Representation Learning via Random                         Convolutions. https:\/\/openreview.net\/forum?id=BVSM0x3EDK6. Published 2021.                         Accessed June 28, 2024."},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3100536"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821"},{"key":"r17","unstructured":"Ji Y, Bai H, Ge C, et                             al. AMOS: a large-scale                         abdominal multi-organ benchmark for versatile medical image                         segmentation. In: NIPS\u201922:                         Proceedings of the 36th International Conference on Neural Information                         Processing Systems. Curran                         Associates, 2022; 36722\u201336732. https:\/\/dl.acm.org\/doi\/10.5555\/3600270.3602931."},{"key":"r18","unstructured":"IXI Dataset \u2013 Brain                         Development. http:\/\/brain-development.org\/ixi-dataset\/. Accessed December                         14, 2024."},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-011-9109-y"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3224067"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024"},{"key":"r22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87564-6"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"r24","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-02151-z"},{"key":"r25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"r26","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P. SegFormer:                         simple and efficient design for semantic segmentation with                         transformers. In: NIPS\u201921:                         Proceedings of the 35th International Conference on Neural Information                         Processing Systems. Curran                         Associates, 2021; 12077\u201312090. https:\/\/dl.acm.org\/doi\/10.5555\/3540261.3541185."},{"key":"r27","first-page":"1623","volume":"227","author":"Wu J","year":"2024","journal-title":"PMLR"}],"container-title":["Radiology: Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/pubs.rsna.org\/doi\/pdf\/10.1148\/ryai.240502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T14:52:24Z","timestamp":1768402344000},"score":1,"resource":{"primary":{"URL":"http:\/\/pubs.rsna.org\/doi\/10.1148\/ryai.240502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,1]]}},"alternative-id":["10.1148\/ryai.240502"],"URL":"https:\/\/doi.org\/10.1148\/ryai.240502","relation":{},"ISSN":["2638-6100"],"issn-type":[{"value":"2638-6100","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"article-number":"e240502"}}