{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:08:36Z","timestamp":1776334116390,"version":"3.51.2"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["11801511"],"award-info":[{"award-number":["11801511"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY22A010003"],"award-info":[{"award-number":["LY22A010003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s10489-026-07240-y","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:33:18Z","timestamp":1776331998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FMD-TransUNet: abdominal multi-organ segmentation based on frequency domain multi-axis representation learning and dual attention mechanisms"],"prefix":"10.1007","volume":"56","author":[{"given":"Fang","family":"Lu","sequence":"first","affiliation":[]},{"given":"Jingyu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Qinxiu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Qiong","family":"Lou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"7240_CR1","unstructured":"Lyu P, Wang C, Zhu J et al (2023) Application of artificial intelligence algorithms for medical image multi-organ segmentation in the field of medicine. Progress Pharmaceut Sci 47(10):751\u2013757. https:\/\/link.cnki.net\/doi\/10.20053\/j.issn1001-5094.2023.10.005"},{"key":"7240_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101843","volume":"88","author":"I Iqbal","year":"2021","unstructured":"Iqbal I, Younus M, Walayat K et al (2021) Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Comput Med Imaging Graph 88:101843. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101843","journal-title":"Comput Med Imaging Graph"},{"issue":"10","key":"7240_CR3","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1049\/iet-ipr.2019.1646","volume":"14","author":"I Iqbal","year":"2020","unstructured":"Iqbal I, Shahzad G, Rafiq N et al (2020) Deep learning-based automated detection of human knee joint\u2019s synovial fluid from magnetic resonance images with transfer learning. IET Image Proc 14(10):1990\u20131998. https:\/\/doi.org\/10.1049\/iet-ipr.2019.1646","journal-title":"IET Image Proc"},{"key":"7240_CR4","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention. Springer, pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"7240_CR5","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N et al (2020) UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"7240_CR6","unstructured":"Oktay O, Schlemper J, Folgoc LL et al (2018) Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"7240_CR7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis FI, Waldner F, Caccetta P et al (2020) ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens 162:94\u2013114. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.01.013","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"7240_CR8","doi-asserted-by":"publisher","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS et al (2016) 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention. Springer, pp 424-432. https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"7240_CR9","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D et al (2017) The one hundred layers tiramisu: Fully convolutional DenseNets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 11-19","DOI":"10.1109\/CVPRW.2017.156"},{"issue":"10","key":"7240_CR10","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H et al (2019) CE-Net: Context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"7240_CR11","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, p 30"},{"key":"7240_CR12","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"7240_CR13","unstructured":"Chen J, Lu Y, Yu Q et al (2021) TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"issue":"3","key":"7240_CR14","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.1609\/aaai.v36i3.20144","volume":"36","author":"H Wang","year":"2022","unstructured":"Wang H, Cao P, Wang J et al (2022) Uctransnet: Rethinking the skip connections in U-Net from a channel-wise perspective with transformer. Proc AAAI Conf Artif Intell 36(3):2441\u20132449. https:\/\/doi.org\/10.1609\/aaai.v36i3.20144","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7240_CR15","doi-asserted-by":"publisher","unstructured":"Cao H, Wang Y, Chen J et al (2022) Swin-Unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision. Cham: Springer Nature Switzerland, pp 205-218. https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"7240_CR16","doi-asserted-by":"publisher","first-page":"1398237","DOI":"10.3389\/fbioe.2024.1398237","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun G, Pan Y, Kong W et al (2024) DA-TransUNet: Integrating spatial and channel dual attention with transformer U-net for medical image segmentation. Front Bioeng Biotechnol 12:1398237. https:\/\/doi.org\/10.3389\/fbioe.2024.1398237","journal-title":"Front Bioeng Biotechnol"},{"key":"7240_CR17","unstructured":"Rao Y, Zhao W, Liu B et al (2021) Global Filter networks for image classification. In: Proceedings of the advances in neural information processing systems. vol 34, pp 980-993"},{"key":"7240_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107290","volume":"164","author":"P Li","year":"2023","unstructured":"Li P, Zhou R, He J et al (2023) A global-frequency-domain network for medical image segmentation. Comput Biol Med 164:107290. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107290","journal-title":"Comput Biol Med"},{"key":"7240_CR19","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s10994-024-06728-3","volume":"114","author":"J Ruan","year":"2025","unstructured":"Ruan J, Gao J, Xie M et al (2025) Learning multi-axis representation in frequency domain for medical image segmentation. Mach Learn 114:10. https:\/\/doi.org\/10.1007\/s10994-024-06728-3","journal-title":"Mach Learn"},{"key":"7240_CR20","doi-asserted-by":"crossref","unstructured":"Xiang H, Xu R, Ma J (2023) HM-ViT: Hetero-modal vehicle-to-vehicle cooperative perception with vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 284-295","DOI":"10.1109\/ICCV51070.2023.00033"},{"key":"7240_CR21","doi-asserted-by":"crossref","unstructured":"Gehrig M, Scaramuzza D (2023) Recurrent vision transformers for object detection with event cameras. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13884-13893","DOI":"10.1109\/CVPR52729.2023.01334"},{"key":"7240_CR22","doi-asserted-by":"crossref","unstructured":"Li X, Ding H, Yuan H et al (2024) Transformer-based visual segmentation: A survey. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2024.3434373"},{"key":"7240_CR23","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"HY Zhou","year":"2023","unstructured":"Zhou HY, Guo J, Zhang Y et al (2023) nnFormer: Volumetric medical image segmentation via a 3D transformer. IEEE Trans Image Process 32:4036\u20134045","journal-title":"IEEE Trans Image Process"},{"key":"7240_CR24","doi-asserted-by":"publisher","first-page":"108205","DOI":"10.1109\/ACCESS.2022.3211501","volume":"10","author":"R Azad","year":"2022","unstructured":"Azad R, Al-Antary MT, Heidari M et al (2022) TransNorm: Transformer provides a strong spatial normalization mechanism for a deep segmentation model. IEEE Access 10:108205\u2013108215","journal-title":"IEEE Access"},{"key":"7240_CR25","doi-asserted-by":"publisher","unstructured":"Zhang S, Xu Y, Wu Z et al (2023) CTC-Net: A novel coupled feature-enhanced transformer and inverted convolution network for medical image segmentation. In: Asian conference on pattern recognition. Cham: Springer Nature Switzerland, pp 273-283. https:\/\/doi.org\/10.1007\/978-3-031-47637-2_21","DOI":"10.1007\/978-3-031-47637-2_21"},{"key":"7240_CR26","doi-asserted-by":"crossref","unstructured":"Heidari M, Kazerouni A, Soltany M et al (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 6202-6212","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"7240_CR27","unstructured":"Rao Y, Zhao W, Zhu Z et al (2021) Global filter networks for image classification. In: Advances in neural information processing systems, pp 34:980-993"},{"key":"7240_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106280","volume":"175","author":"Y Chen","year":"2024","unstructured":"Chen Y, Zhang X, Peng L et al (2024) Medical image segmentation network based on multi-scale frequency domain filter. Neural Netw 175:106280. https:\/\/doi.org\/10.1016\/j.neunet.2024.106280","journal-title":"Neural Netw"},{"key":"7240_CR29","doi-asserted-by":"publisher","unstructured":"Tang S, Ran H, Yang S et al (2024) A frequency selection network for medical image segmentation. Heliyon 10(16). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e35698. External Link","DOI":"10.1016\/j.heliyon.2024.e35698"},{"key":"7240_CR30","doi-asserted-by":"crossref","unstructured":"Liu S, Lin Y, Liu D et al (2025) Frequency-enhanced lightweight vision mamba network for medical image segmentation. IEEE Trans Instrum Meas","DOI":"10.1109\/TIM.2025.3527526"},{"key":"7240_CR31","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE, pp 3146-3154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"7240_CR32","doi-asserted-by":"crossref","unstructured":"Jha D, Riegler MA, Johansen D et al (2020) Doubleu-net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd international symposium on computer-based medical systems. IEEE, pp 558\u2013564","DOI":"10.1109\/CBMS49503.2020.00111"},{"issue":"1","key":"7240_CR33","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1038\/s41598-023-28530-2","volume":"13","author":"J Lewis","year":"2023","unstructured":"Lewis J, Cha YJ, Kim J (2023) Dual encoder\u2013decoder-based deep polyp segmentation network for colonoscopy images. Sci Rep 13(1):1183. https:\/\/doi.org\/10.1038\/s41598-023-28530-2","journal-title":"Sci Rep"},{"key":"7240_CR34","doi-asserted-by":"publisher","unstructured":"Ding M, Xiao B, Codella N et al (2022) Davit: Dual attention vision transformers. In: European conference on computer vision. Cham: Springer Nature Switzerland, pp 74-92. https:\/\/doi.org\/10.1007\/978-3-031-20053-3_5","DOI":"10.1007\/978-3-031-20053-3_5"},{"key":"7240_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107139","volume":"126","author":"GC Ates","year":"2023","unstructured":"Ates GC, Mohan P, Celik E (2023) Dual cross-attention for medical image segmentation. Eng Appl Artif Intell 126:107139. https:\/\/doi.org\/10.1016\/j.engappai.2023.107139","journal-title":"Eng Appl Artif Intell"},{"key":"7240_CR36","doi-asserted-by":"crossref","unstructured":"Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"7240_CR37","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M et al (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"7240_CR38","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"7240_CR39","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"7240_CR40","doi-asserted-by":"publisher","unstructured":"Drozdzal M, Vorontsov E, Chartrand G et al (2016) The importance of skip connections in biomedical image segmentation. International workshop on deep learning in medical image analysis. Cham: Springer International Publishing, pp 179-187. https:\/\/doi.org\/10.1007\/978-3-319-46976-8_19","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"7240_CR41","unstructured":"Landman B, Xu Z, Igelsias J et al (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"},{"issue":"11","key":"7240_CR42","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 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":"7240_CR43","doi-asserted-by":"crossref","unstructured":"Huang Y (2023) ViT-R50 GAN: Vision transformers hybrid model based generative adversarial networks for image generation. In: 2023 3rd International conference on consumer electronics and computer engineering, pp 590\u2013593","DOI":"10.1109\/ICCECE58074.2023.10135253"},{"key":"7240_CR44","doi-asserted-by":"crossref","unstructured":"Jha A, Kumar A, Pande S et al (2020) MT-UNet: A novel U-Net based multi-task architecture for visual scene understanding. In: Proc IEEE int conf image process (ICIP), pp 2191\u20132195","DOI":"10.1109\/ICIP40778.2020.9190695"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07240-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07240-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07240-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:33:43Z","timestamp":1776332023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07240-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":44,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["7240"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07240-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"30 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","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 declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"211"}}