{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:48:25Z","timestamp":1758311305974,"version":"3.44.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012422","name":"Anhui Provincial University Teaching Quality and Teaching Reform Project","doi-asserted-by":"publisher","award":["2021zdjgxm022","2021jyxm1263"],"award-info":[{"award-number":["2021zdjgxm022","2021jyxm1263"]}],"id":[{"id":"10.13039\/501100012422","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10489-025-06554-7","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T01:35:55Z","timestamp":1751333755000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SWADA: slide-window-based active domain adaptation for cross-modality medical image segmentation"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7999-2971","authenticated-orcid":false,"given":"Yongjun","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongmei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijian","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"issue":"1","key":"6554_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17478-w","volume":"11","author":"DC Castro","year":"2020","unstructured":"Castro DC, Walker I, Glocker B (2020) Causality matters in medical imaging. Nature Commun 11(1):1\u201310","journal-title":"Nature Commun"},{"issue":"1","key":"6554_CR2","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Mach Learn 79(1):151\u2013175","journal-title":"Mach Learn"},{"key":"6554_CR3","doi-asserted-by":"crossref","unstructured":"Ning M, Lu D, Wei D, Bian C, Yuan C, Yu S, Ma K, Zheng Y (2021) Multi-anchor active domain adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9112\u20139122","DOI":"10.1109\/ICCV48922.2021.00898"},{"key":"6554_CR4","doi-asserted-by":"crossref","unstructured":"Shin I, Kim D-J, Cho JW, Woo S, Park K, Kweon IS (2021) Labor: Labeling only if required for domain adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8588\u20138598","DOI":"10.1109\/ICCV48922.2021.00847"},{"key":"6554_CR5","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. Adv Neural Inf Process Syst 31"},{"key":"6554_CR6","doi-asserted-by":"crossref","unstructured":"You K, Long M, Cao Z, Wang J, Jordan MI (2019) Universal domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2720\u20132729","DOI":"10.1109\/CVPR.2019.00283"},{"key":"6554_CR7","doi-asserted-by":"crossref","unstructured":"Chen Y, Li W, Sakaridis C, Dai D, Van\u00a0Gool L (2018) Domain adaptive faster r-cnn for object detection in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3339\u20133348","DOI":"10.1109\/CVPR.2018.00352"},{"key":"6554_CR8","doi-asserted-by":"crossref","unstructured":"Vs V, Gupta V, Oza P, Sindagi VA, Patel VM (2021) Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4516\u20134526","DOI":"10.1109\/CVPR46437.2021.00449"},{"key":"6554_CR9","doi-asserted-by":"crossref","unstructured":"Li Y, Yuan L, Vasconcelos N (2019) Bidirectional learning for domain adaptation of semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6936\u20136945","DOI":"10.1109\/CVPR.2019.00710"},{"key":"6554_CR10","doi-asserted-by":"crossref","unstructured":"Yang Y, Soatto S (2020) Fda: Fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4085\u20134095","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"6554_CR11","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"6554_CR12","doi-asserted-by":"crossref","unstructured":"Saito K, Kim D, Sclaroff S, Darrell T, Saenko K (2019) Semi-supervised domain adaptation via minimax entropy. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8050\u20138058","DOI":"10.1109\/ICCV.2019.00814"},{"key":"6554_CR13","doi-asserted-by":"crossref","unstructured":"Li B, Wang Y, Zhang S, Li D, Keutzer K, Darrell T, Zhao H (2021) Learning invariant representations and risks for semi-supervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1104\u20131113","DOI":"10.1109\/CVPR46437.2021.00116"},{"key":"6554_CR14","unstructured":"Liang J, Hu D, Feng J (2020) Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: International conference on machine learning, PMLR, pp 6028\u20136039"},{"key":"6554_CR15","doi-asserted-by":"publisher","first-page":"2518","DOI":"10.1109\/TIP.2022.3157139","volume":"31","author":"B Xu","year":"2022","unstructured":"Xu B, Zeng Z, Lian C, Ding Z (2022) Few-shot domain adaptation via mixup optimal transport. IEEE Trans Image Process 31:2518\u20132528","journal-title":"IEEE Trans Image Process"},{"key":"6554_CR16","unstructured":"Settles B (2009) Active learning literature survey"},{"key":"6554_CR17","unstructured":"Schohn G, Cohn D (2000) Less is more: Active learning with support vector machines. In: ICML, Citeseer, pp 6"},{"key":"6554_CR18","doi-asserted-by":"crossref","unstructured":"Jain SD, Grauman K (2016) Active image segmentation propagation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2864\u20132873","DOI":"10.1109\/CVPR.2016.313"},{"key":"6554_CR19","doi-asserted-by":"crossref","unstructured":"Sinha S, Ebrahimi S, Darrell T (2019) Variational adversarial active learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5972\u20135981","DOI":"10.1109\/ICCV.2019.00607"},{"key":"6554_CR20","unstructured":"Ash JT, Zhang C, Krishnamurthy A, Langford J, Agarwal A (2019) Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv:1906.03671"},{"key":"6554_CR21","unstructured":"Casanova A, Pinheiro PO, Rostamzadeh N, Pal CJ (2020) Reinforced active learning for image segmentation. arXiv:2002.06583"},{"key":"6554_CR22","doi-asserted-by":"crossref","unstructured":"Fu B, Cao Z, Wang J, Long M (2021) Transferable query selection for active domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7272\u20137281","DOI":"10.1109\/CVPR46437.2021.00719"},{"key":"6554_CR23","doi-asserted-by":"crossref","unstructured":"Prabhu V, Chandrasekaran A, Saenko K, Hoffman J (2021) Active domain adaptation via clustering uncertainty-weighted embeddings. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8505\u20138514","DOI":"10.1109\/ICCV48922.2021.00839"},{"key":"6554_CR24","doi-asserted-by":"crossref","unstructured":"Su J-C, Tsai Y-H, Sohn K, Liu B, Maji S, Chandraker M (2020) Active adversarial domain adaptation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 739\u2013748","DOI":"10.1109\/WACV45572.2020.9093390"},{"key":"6554_CR25","doi-asserted-by":"crossref","unstructured":"Xie B, Yuan L, Li S, Liu CH, Cheng X (2022) Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8068\u20138078","DOI":"10.1109\/CVPR52688.2022.00790"},{"key":"6554_CR26","doi-asserted-by":"crossref","unstructured":"Wu T-H, Liou Y-S, Yuan S-J, Lee H-Y, Chen T-I, Huang K-C, Hsu WH (2022) D 2 ada: Dynamic density-aware active domain adaptation for semantic segmentation. In: European conference on computer vision, Springer, pp 449\u2013467","DOI":"10.1007\/978-3-031-19818-2_26"},{"key":"6554_CR27","doi-asserted-by":"crossref","unstructured":"Liu Q, Chen C, Qin J, Dou Q, Heng P-A (2021) Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1013\u20131023","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"6554_CR28","unstructured":"Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A (2017) Multi-atlas labeling beyond the cranial vault-workshop and challenge. https:\/\/www.synapse.org\/Synapse:syn3193805\/wiki\/217789"},{"key":"6554_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101950","volume":"69","author":"AE Kavur","year":"2021","unstructured":"Kavur AE, Gezer NS, Bar\u0131\u015f M, Aslan S, Conze P-H, Groza V, Pham DD, Chatterjee S, Ernst P, \u00d6zkan S et al (2021) Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Med Image Anal 69:101950","journal-title":"Med Image Anal"},{"key":"6554_CR30","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang X, Shen J (2016) Multi-scale patch and multi-modality atlases for whole heart segmentation of mri. Med Image Anal 31:77\u201387","journal-title":"Med Image Anal"},{"issue":"10","key":"6554_CR31","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"issue":"7","key":"6554_CR32","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","volume":"39","author":"C Chen","year":"2020","unstructured":"Chen C, Dou Q, Chen H, Qin J, Heng PA (2020) Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Ttrans Med Imaging 39(7):2494\u20132505","journal-title":"IEEE Ttrans Med Imaging"},{"issue":"1","key":"6554_CR33","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TMI.2021.3105046","volume":"41","author":"X Han","year":"2021","unstructured":"Han X, Qi L, Yu Q, Zhou Z, Zheng Y, Shi Y, Gao Y (2021) Deep symmetric adaptation network for cross-modality medical image segmentation. IEEE Trans Med Imaging 41(1):121\u2013132","journal-title":"IEEE Trans Med Imaging"},{"key":"6554_CR34","doi-asserted-by":"crossref","unstructured":"Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D (2020) Self-supervision with superpixels: Training few-shot medical image segmentation without annotation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX 16, Springer, pp 762\u2013780","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"6554_CR35","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition, Ieee, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6554_CR36","doi-asserted-by":"crossref","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"6554_CR37","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"6554_CR38","doi-asserted-by":"crossref","unstructured":"Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"6554_CR39","unstructured":"Hoffman J, Tzeng E, Park T, Zhu J-Y, Isola P, Saenko K, Efros A, Darrell T (2018) Cycada: Cycle-consistent adversarial domain adaptation. In: International conference on machine learning, Pmlr, pp 1989\u20131998"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06554-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06554-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06554-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T15:55:25Z","timestamp":1758297325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06554-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":39,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6554"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06554-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"8 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}},{"value":"Consent for publication The paper is original in its contents and is not under consideration for publication in any other journals\/proceedings. The datasets generated during and\/or analyzed during the current study are available from the corresponding author upon reasonable request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"831"}}