{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:06:40Z","timestamp":1774868800224,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62003065"],"award-info":[{"award-number":["No. 62003065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005230","name":"the Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["No. CSTB2024NSCQ-MSX0527"],"award-info":[{"award-number":["No. CSTB2024NSCQ-MSX0527"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Science and Technology Research Program of Chongqing Municipal Education Commission","award":["No. KJQN202200564"],"award-info":[{"award-number":["No. KJQN202200564"]}]},{"name":"the Science and Technology Research Program of Chongqing Municipal Education Commission","award":["No. KJZD-K202200504"],"award-info":[{"award-number":["No. KJZD-K202200504"]}]},{"name":"the Fund project of Chongqing Normal University","award":["No. 21XLB032"],"award-info":[{"award-number":["No. 21XLB032"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10489-025-06987-0","type":"journal-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T09:26:14Z","timestamp":1766222774000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MS-DCSNet: Global-local feature interaction and multi-scale dynamic channel shuffle attention for medical image segmentation"],"prefix":"10.1007","volume":"56","author":[{"given":"Hao","family":"Zhai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9017-6564","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"issue":"7553","key":"6987_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"6987_CR2","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"6987_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u2013211","journal-title":"Nat Methods"},{"key":"6987_CR4","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"6987_CR5","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J (2020) Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1055\u20131059. IEEE","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"6987_CR6","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, vol 25"},{"key":"6987_CR7","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"6987_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6987_CR9","doi-asserted-by":"crossref","unstructured":"Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S (2023) Convnext v2: co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16133\u201316142","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"6987_CR10","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"6987_CR11","doi-asserted-by":"crossref","unstructured":"Chen S, Tan X, Wang B, Hu X (2018) Reverse attention for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 234\u2013250","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"6987_CR12","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6987_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124698","volume":"255","author":"L Wang","year":"2024","unstructured":"Wang L, Xu P, Cao X, Nappi M, Wan S (2024) Label-aware attention network with multi-scale boosting for medical image segmentation. Expert Syst Appl 255:124698","journal-title":"Expert Syst Appl"},{"key":"6987_CR14","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, vol 30"},{"key":"6987_CR15","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929"},{"issue":"5","key":"6987_CR16","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TMI.2022.3230943","volume":"42","author":"X Huang","year":"2022","unstructured":"Huang X, Deng Z, Li D, Yuan X, Fu Y (2022) Missformer: an effective transformer for 2d medical image segmentation. IEEE Trans Med Imaging 42(5):1484\u20131494","journal-title":"IEEE Trans Med Imaging"},{"key":"6987_CR17","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv:2102.04306"},{"key":"6987_CR18","doi-asserted-by":"publisher","first-page":"102634","DOI":"10.1016\/j.inffus.2024.102634","volume":"113","author":"X Liu","year":"2025","unstructured":"Liu X, Gao P, Yu T, Wang F, Yuan R-Y (2025) Cswin-unet: transformer unet with cross-shaped windows for medical image segmentation. Inf Fusion 113:102634. https:\/\/doi.org\/10.1016\/j.inffus.2024.102634","journal-title":"Inf Fusion"},{"key":"6987_CR19","doi-asserted-by":"crossref","unstructured":"Azad R, Arimond R, Aghdam EK, Kazerouni A, Merhof D (2023) Dae-former: dual attention-guided efficient transformer for medical image segmentation. In: International workshop on predictive intelligence in medicine, pp 83\u201395. Springer","DOI":"10.1007\/978-3-031-46005-0_8"},{"issue":"9","key":"6987_CR20","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","volume":"42","author":"A He","year":"2023","unstructured":"He A, Wang K, Li T, Du C, Xia S, Fu H (2023) H2former: an efficient hierarchical hybrid transformer for medical image segmentation. IEEE Trans Med Imaging 42(9):2763\u20132775","journal-title":"IEEE Trans Med Imaging"},{"key":"6987_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu H, Hu Q (2021) Transfuse: fusing transformers and cnns for medical image segmentation. In: Medical Image Computing and Computer Assisted intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp 14\u201324. Springer","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"6987_CR22","unstructured":"Zhou H-Y, Guo J, Zhang Y, Yu L, Wang L, Yu Y (2021) nnformer: interleaved transformer for volumetric segmentation. arXiv:2109.03201"},{"key":"6987_CR23","doi-asserted-by":"crossref","unstructured":"Ruan J, Li J, Xiang S (2024) Vm-unet: vision mamba unet for medical image segmentation. arXiv:2402.02491","DOI":"10.1145\/3767748"},{"key":"6987_CR24","doi-asserted-by":"crossref","unstructured":"Liu J, Yang H, Zhou H-Y, Xi Y, Yu L, Li C, Liang Y, Shi G, Yu Y, Zhang S et al (2024) Swin-umamba: Mamba-based unet with imagenet-based pretraining. In: International conference on medical image computing and computer-assisted intervention, pp 615\u2013625. Springer","DOI":"10.1007\/978-3-031-72114-4_59"},{"key":"6987_CR25","doi-asserted-by":"crossref","unstructured":"Chen C, Yu L, Min S, Wang S (2024) Msvm-unet: multi-scale vision mamba unet for medical image segmentation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 3111\u20133114. IEEE","DOI":"10.1109\/BIBM62325.2024.10821761"},{"key":"6987_CR26","doi-asserted-by":"crossref","unstructured":"Wang Z, Zheng J-Q, Zhang Y, Cui G, Li L (2024) Mamba-unet: unet-like pure visual mamba for medical image segmentation. arXiv:2402.05079","DOI":"10.2139\/ssrn.5097998"},{"key":"6987_CR27","doi-asserted-by":"crossref","unstructured":"Yu W, Wang X (2025) Mambaout: do we really need mamba for vision? In: Proceedings of the computer vision and pattern recognition conference, pp 4484\u20134496","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"6987_CR28","unstructured":"Qu H, Ning L, An R, Fan W, Derr T, Liu H, Xu X, Li Q (2024) A survey of mamba. arXiv:2408.01129"},{"key":"6987_CR29","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"issue":"5","key":"6987_CR30","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"6987_CR31","doi-asserted-by":"publisher","first-page":"999","DOI":"10.28991\/ESJ-2024-08-03-012","volume":"8","author":"SN Nobel","year":"2024","unstructured":"Nobel SN, Sifat OF, Islam MR, Sayeed MS, Amiruzzaman M (2024) Enhancing gi cancer radiation therapy: advanced organ segmentation with reseca-u-net model. Emerg Sci J 8(3):999\u20131015","journal-title":"Emerg Sci J"},{"key":"6987_CR32","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, pp 424\u2013432. Springer","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"6987_CR33","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision, pp 205\u2013218. Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"6987_CR34","doi-asserted-by":"crossref","unstructured":"Wang H, Xie S, Lin L, Iwamoto Y, Han X-H, Chen Y-W, Tong R (2022) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 2390\u20132394. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"key":"6987_CR35","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"6987_CR36","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2021) Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion workshop, pp 272\u2013284. Springer","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"6987_CR37","unstructured":"Gu A, Dao T (2023) Mamba: linear-time sequence modeling with selective state spaces. arXiv:2312.00752"},{"key":"6987_CR38","doi-asserted-by":"crossref","unstructured":"Peng B, Alcaide E, Anthony Q, Albalak A, Arcadinho S, Biderman S, Cao H, Cheng X, Chung M, Grella M et al (2023) Rwkv: reinventing rnns for the transformer era. arXiv:2305.13048","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"6987_CR39","unstructured":"Duan Y, Wang W, Chen Z, Zhu X, Lu L, Lu T, Qiao Y, Li H, Dai J, Wang W (2024) Vision-rwkv: efficient and scalable visual perception with RWKV-like architectures. arXiv:2403.02308"},{"key":"6987_CR40","unstructured":"Ma J, Li F, Wang B (2024) U-mamba: enhancing long-range dependency for biomedical image segmentation. arXiv:2401.04722"},{"key":"6987_CR41","doi-asserted-by":"crossref","unstructured":"Xing Z, Ye T, Yang Y, Liu G, Zhu L (2024) Segmamba: long-range sequential modeling mamba for 3d medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 578\u2013588. Springer","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"6987_CR42","unstructured":"Jiang J, Zhang J, Liu W, Gao M, Hu X, Yan X, Huang F, Liu Y (2025) Rwkv-unet: improving unet with long-range cooperation for effective medical image segmentation. arXiv:2501.08458"},{"key":"6987_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3561797","author":"T Chen","year":"2025","unstructured":"Chen T, Zhou X, Tan Z, Wu Y, Wang Z, Ye Z, Gong T, Chu Q, Yu N, Lu L (2025) Zig-rir: zigzag rwkv-in-rwkv for efficient medical image segmentation. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2025.3561797","journal-title":"IEEE Trans Med Imaging"},{"key":"6987_CR44","doi-asserted-by":"crossref","unstructured":"Wang J, Chen J, Chen D, Wu J (2024) Lkm-unet: large kernel vision mamba unet for medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 360\u2013370. Springer","DOI":"10.1007\/978-3-031-72111-3_34"},{"key":"6987_CR45","doi-asserted-by":"crossref","unstructured":"Chen C-FR, Fan Q, Panda R (2021) Crossvit: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"issue":"4","key":"6987_CR46","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6987_CR47","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"6987_CR48","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"6987_CR49","unstructured":"Zhang Z, Zhang W (2021) Pyramid medical transformer for medical image segmentation. arXiv:2104.14702"},{"issue":"10","key":"6987_CR50","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"6987_CR51","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.28991\/ESJ-2024-08-06-014","volume":"8","author":"S Armoogum","year":"2024","unstructured":"Armoogum S, Motean K, Dewi DA, Kurniawan TB, Kijsomporn J (2024) Breast cancer prediction using transfer learning-based classification model. Emerg Sci J 8(6):2373\u20132384","journal-title":"Emerg Sci J"},{"key":"6987_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863","journal-title":"Data Brief"},{"key":"6987_CR53","doi-asserted-by":"crossref","unstructured":"Heidari M, Kazerouni A, Soltany M, Azad R, Aghdam EK, Cohen-Adad J, Merhof D (2023) Hiformer: hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 6202\u20136212","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"6987_CR54","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, pp 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6987_CR55","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"6987_CR56","unstructured":"Yue Y, Li Z (2024) Medmamba: vision mamba for medical image classification. arXiv:2403.03849"},{"key":"6987_CR57","doi-asserted-by":"crossref","unstructured":"Rahman MM, Munir M, Marculescu R (2024) Emcad: efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11769\u201311779","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"6987_CR58","unstructured":"Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A (2015) Miccai multi-atlas labeling beyond the cranial vault\u2013workshop and challenge. In: Proc. MICCAI multi-atlas labeling beyond cranial vault\u2014workshop challenge, vol 5, p 12. Munich, Germany"},{"issue":"11","key":"6987_CR59","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"key":"6987_CR60","doi-asserted-by":"crossref","unstructured":"Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 168\u2013172. IEEE","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"6987_CR61","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111","journal-title":"Comput Med Imaging Graph"},{"key":"6987_CR62","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: learning where to look for the pancreas. arXiv:1804.03999"},{"key":"6987_CR63","doi-asserted-by":"crossref","unstructured":"Qin J, Yu J, Xiang J, He X, Zhang W, Wu L (2023) Aia-unet: attention in attention for medical image segmentation. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 2179\u20132182. IEEE","DOI":"10.1109\/BIBM58861.2023.10385828"},{"key":"6987_CR64","doi-asserted-by":"crossref","unstructured":"Rahman MM, Marculescu R (2023) Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 6222\u20136231","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"6987_CR65","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06987-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06987-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06987-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:18:32Z","timestamp":1774865912000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06987-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["6987"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06987-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,20]]},"assertion":[{"value":"1 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2025","order":3,"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 relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"15"}}