{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T12:40:12Z","timestamp":1766148012949,"version":"3.48.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-CA246704"],"award-info":[{"award-number":["R01-CA246704"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01-CA268808"],"award-info":[{"award-number":["U01-CA268808"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R03-EB032943"],"award-info":[{"award-number":["R03-EB032943"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R15-EB030356"],"award-info":[{"award-number":["R15-EB030356"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01-DK127384-02S1"],"award-info":[{"award-number":["U01-DK127384-02S1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-CA240639"],"award-info":[{"award-number":["R01-CA240639"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.media.2025.103848","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T06:49:08Z","timestamp":1761202148000},"page":"103848","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["AdverIN: Monotonic adversarial intensity attack for domain generalization in medical image segmentation"],"prefix":"10.1016","volume":"107","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-9240","authenticated-orcid":false,"given":"Zheyuan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0694-442X","authenticated-orcid":false,"given":"Lanhong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Elif","family":"Keles","sequence":"additional","affiliation":[]},{"given":"Debesh","family":"Jha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-1952","authenticated-orcid":false,"given":"Matthew","family":"Antalek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-1955","authenticated-orcid":false,"given":"Gorkem","family":"Durak","sequence":"additional","affiliation":[]},{"given":"Alpay","family":"Medetalibeyoglu","sequence":"additional","affiliation":[]},{"given":"Concetto","family":"Spampinato","sequence":"additional","affiliation":[]},{"given":"Baris","family":"Turkbey","sequence":"additional","affiliation":[]},{"given":"Boqing","family":"Gong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7379-6829","authenticated-orcid":false,"given":"Ulas","family":"Bagci","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.media.2025.103848_bib0001","doi-asserted-by":"crossref","unstructured":"[Akrout et al., 2023] Akrout, M., Gyepesi, B., Holl\u00f3, P., Po\u00f3r, A., Kincs\u0151, B., Solis, S., Cirone, K., Kawahara, J., Slade, D., Abid, L., et al., 2023. Diffusion-based data augmentation for skin disease classification: impact across original medical datasets to fully synthetic images. arXiv: 2301.04802.","DOI":"10.1007\/978-3-031-53767-7_10"},{"key":"10.1016\/j.media.2025.103848_bib0002","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1002\/9781119790686.ch6","article-title":"Building blocks of AI","author":"Bagci","year":"2023","journal-title":"AI Clin. Med. A Pract. Guide Healthcare Prof."},{"key":"10.1016\/j.media.2025.103848_bib0003","unstructured":"[Billot et al., 2020] Billot, B., Greve, D., Van Leemput, K., Fischl, B., Iglesias, J. E., Dalca, A. V., 2020. A learning strategy for contrast-agnostic MRI segmentation. arXiv: 2003.01995."},{"key":"10.1016\/j.media.2025.103848_bib0004","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"4300","article-title":"Optimizing neural network effectiveness via non-monotonicity refinement","author":"Biswas","year":"2025"},{"key":"10.1016\/j.media.2025.103848_bib0005","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"21438","article-title":"UniverSeg: universal medical image segmentation","author":"Butoi","year":"2023"},{"key":"10.1016\/j.media.2025.103848_bib0006","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part V","first-page":"151","article-title":"MaxStyle: adversarial style composition for robust medical image segmentation","author":"Chen","year":"2022"},{"key":"10.1016\/j.media.2025.103848_bib0007","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23","first-page":"667","article-title":"Realistic adversarial data augmentation for MR image segmentation","author":"Chen","year":"2020"},{"key":"10.1016\/j.media.2025.103848_bib0008","series-title":"International Conference on Learning Representations","article-title":"Sharpness-aware minimization for efficiently improving generalization","author":"Foret","year":"2021"},{"key":"10.1016\/j.media.2025.103848_bib0009","unstructured":"[Goodfellow et al., 2014] Goodfellow, I. J., Shlens, J., Szegedy, C., 2014. Explaining and harnessing adversarial examples. arXiv: 1412.6572."},{"key":"10.1016\/j.media.2025.103848_bib0010","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"15262","article-title":"Natural adversarial examples","author":"Hendrycks","year":"2021"},{"issue":"3","key":"10.1016\/j.media.2025.103848_bib0011","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/TMI.2021.3116879","article-title":"SynthMorph: learning contrast-invariant registration without acquired images","volume":"41","author":"Hoffmann","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103848_bib0012","series-title":"Proceedings of the IEEE International Conference on Computer Vision (CVPR)","first-page":"1501","article-title":"Arbitrary style transfer in real-time with adaptive instance normalization","author":"Huang","year":"2017"},{"key":"10.1016\/j.media.2025.103848_bib0013","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part II 16","first-page":"124","article-title":"Self-challenging improves cross-domain generalization","author":"Huang","year":"2020"},{"issue":"2","key":"10.1016\/j.media.2025.103848_bib0014","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering12020180","article-title":"A conceptual framework for applying ethical principles of AI to medical practice","volume":"12","author":"Jha","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.media.2025.103848_bib0015","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"9176","article-title":"Frequency-domain refinement of vision transformers for robust medical image segmentation under degradation","author":"Karimijarbigloo","year":"2025"},{"key":"10.1016\/j.media.2025.103848_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion models in medical imaging: a comprehensive survey","volume":"88","author":"Kazerouni","year":"2023","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.media.2025.103848_bib0017","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.yacr.2022.04.010","article-title":"Musculoskeletal MR image segmentation with artificial intelligence","volume":"4","author":"Keles","year":"2022","journal-title":"Adv. Clin. Radiol."},{"issue":"9","key":"10.1016\/j.media.2025.103848_bib0018","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202400044","article-title":"Deformable capsules for object detection","volume":"6","author":"LaLonde","year":"2024","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.media.2025.103848_bib0019","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Domain generalization with adversarial feature learning","author":"Li","year":"2018"},{"key":"10.1016\/j.media.2025.103848_bib0020","series-title":"International Conference on Learning Representations","article-title":"Uncertainty modeling for out-of-distribution generalization","author":"Li","year":"2022"},{"key":"10.1016\/j.media.2025.103848_bib0021","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1013","article-title":"FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space","author":"Liu","year":"2021"},{"key":"10.1016\/j.media.2025.103848_bib0022","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part II 23","first-page":"475","article-title":"Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains","author":"Liu","year":"2020"},{"issue":"12","key":"10.1016\/j.media.2025.103848_bib0023","doi-asserted-by":"crossref","first-page":"3699","DOI":"10.1109\/TMI.2022.3193146","article-title":"AADG: automatic augmentation for domain generalization on retinal image segmentation","volume":"41","author":"Lyu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2025.103848_bib0024","article-title":"Foundational artificial intelligence models and modern medical practice","volume":"2","author":"Medetalibeyoglu","year":"2025","journal-title":"BJR Artif. Intell."},{"key":"10.1016\/j.media.2025.103848_bib0025","series-title":"International Conference on Learning Representations","article-title":"Minimal-entropy correlation alignment for unsupervised deep domain adaptation","author":"Morerio","year":"2018"},{"issue":"8","key":"10.1016\/j.media.2025.103848_bib0026","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1038\/nn886","article-title":"Spatiotemporal mechanisms for detecting and identifying image features in human vision","volume":"5","author":"Neri","year":"2002","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.media.2025.103848_bib0027","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"2830","article-title":"Generalization by adaptation: diffusion-based domain extension for domain-generalized semantic segmentation","author":"Niemeijer","year":"2024"},{"key":"10.1016\/j.media.2025.103848_bib0028","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9482","article-title":"Permuted adaIN: reducing the bias towards global statistics in image classification","author":"Nuriel","year":"2021"},{"key":"10.1016\/j.media.2025.103848_bib0029","series-title":"International Conference on Machine Learning","first-page":"5389","article-title":"Do imagenet classifiers generalize to imagenet?","author":"Recht","year":"2019"},{"issue":"1","key":"10.1016\/j.media.2025.103848_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.yacr.2023.06.001","article-title":"A comprehensive review of deep learning approaches for magnetic resonance imaging liver tumor analysis","volume":"5","author":"Velichko","year":"2023","journal-title":"Adv. Clin. Radiol."},{"issue":"8","key":"10.1016\/j.media.2025.103848_bib0031","first-page":"8052","article-title":"Generalizing to unseen domains: a survey on domain generalization","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"12","key":"10.1016\/j.media.2025.103848_bib0032","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/TMI.2020.3015224","article-title":"Dofe: domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets","volume":"39","author":"Wang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103848_bib0033","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"834","article-title":"Learning to diversify for single domain generalization","author":"Wang","year":"2021"},{"key":"10.1016\/j.media.2025.103848_bib0034","series-title":"International Conference on Learning Representations","article-title":"Noise or signal: the role of image backgrounds in object recognition","author":"Xiao","year":"2020"},{"key":"10.1016\/j.media.2025.103848_bib0035","series-title":"International Conference on Learning Representations","article-title":"Robust and generalizable visual representation learning via random convolutions","author":"Xu","year":"2021"},{"issue":"5","key":"10.1016\/j.media.2025.103848_bib0036","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1097\/MOG.0000000000000966","article-title":"A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging","volume":"39","author":"Yao","year":"2023","journal-title":"Curr. Opin. Gastroenterol."},{"key":"10.1016\/j.media.2025.103848_bib0037","unstructured":"[Yoon et al., 2023] Yoon, J. S., Oh, K., Shin, Y., Mazurowski, M. A., Suk, H.-I., 2023. Domain generalization for medical image analysis: a survey. arXiv: 2310.08598."},{"key":"10.1016\/j.media.2025.103848_bib0038","series-title":"International Conference on Learning Representations","article-title":"Mixup: beyond empirical risk minimization","author":"Zhang","year":"2018"},{"issue":"7","key":"10.1016\/j.media.2025.103848_bib0039","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","article-title":"Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation","volume":"39","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2025.103848_bib0040","first-page":"31","article-title":"Domain generalization with correlated style uncertainty","volume":"5","author":"Zhang","year":"2024","journal-title":"WACV 2024"},{"issue":"1","key":"10.1016\/j.media.2025.103848_bib0041","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.yacr.2023.05.001","article-title":"Deep learning algorithms for pancreas segmentation from radiology scans: a review","volume":"5","author":"Zhang","year":"2023","journal-title":"Adv. Clin. Radiol."},{"issue":"9","key":"10.1016\/j.media.2025.103848_bib0042","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1109\/TMI.2024.3519307","article-title":"DiffBoost: enhancing medical image segmentation via text-guided diffusion model","volume":"44","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.media.2025.103848_bib0043","first-page":"4396","article-title":"Domain generalization: a survey","volume":"45","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2025.103848_bib0044","series-title":"International Conference on Learning Representations","article-title":"Domain generalization with mixstyle","author":"Zhou","year":"2021"},{"key":"10.1016\/j.media.2025.103848_bib0045","first-page":"21285","article-title":"Towards theoretically understanding why sgd generalizes better than adam in deep learning","volume":"33","author":"Zhou","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2025.103848_bib0046","series-title":"International Conference on Learning Representations","article-title":"Surrogate gap minimization improves sharpness-aware training","author":"Zhuang","year":"2022"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525003949?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525003949?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T12:37:28Z","timestamp":1766147848000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841525003949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":46,"alternative-id":["S1361841525003949"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103848","relation":{},"ISSN":["1361-8415"],"issn-type":[{"type":"print","value":"1361-8415"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AdverIN: Monotonic adversarial intensity attack for domain generalization in medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103848","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103848"}}