{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:17:02Z","timestamp":1783160222078,"version":"3.54.6"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["60974042"],"award-info":[{"award-number":["60974042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007820","name":"Hangzhou Normal University","doi-asserted-by":"publisher","award":["2024JCXK03"],"award-info":[{"award-number":["2024JCXK03"]}],"id":[{"id":"10.13039\/501100007820","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110698","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:19:41Z","timestamp":1780409981000},"page":"110698","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["BiMaxKAN: A KAN-based dual-branch network for medical image segmentation with interpretability analysis"],"prefix":"10.1016","volume":"124","author":[{"given":"Ju","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuwei","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110698_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108112","article-title":"A review of deep learning methods for denoising of low-dose CT images","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110698_b2","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Med. Image Comput. Computer-Assisted Interv. (MICCAI)"},{"key":"10.1016\/j.bspc.2026.110698_b3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","article-title":"UNet++: A nested U-net architecture for medical image segmentation","author":"Zhou","year":"2018","journal-title":"Deep. Learn. Med. Image Anal. Multimodal Learn. Clin. Decis. Support."},{"key":"10.1016\/j.bspc.2026.110698_b4","doi-asserted-by":"crossref","unstructured":"H. Huang, L. Lin, R. Tong, H. Hu, UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation, in: ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2020, pp. 1055\u20131059.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"10.1016\/j.bspc.2026.110698_b5","series-title":"Attention U-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.bspc.2026.110698_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128817","article-title":"Multi-scale adaptive residual cold diffusion model for low-dose CT denoising","volume":"294","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110698_b7","series-title":"TransUNet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110698_b8","series-title":"Swin-Unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2021"},{"key":"10.1016\/j.bspc.2026.110698_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107510","article-title":"TransGraphNet: A novel network for medical image segmentation based on transformer and graph convolution","volume":"104","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110698_b10","first-page":"574","article-title":"UNETR: Transformers for 3D medical image segmentation","author":"Hatamizadeh","year":"2022","journal-title":"IEEE Winter Conf. Appl. Comput. Vis. (WACV)"},{"key":"10.1016\/j.bspc.2026.110698_b11","article-title":"KAN: Kolmogorov\u2013Arnold networks","author":"Liu","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110698_b12","series-title":"U-KAN makes strong backbone for medical image segmentation and generation","author":"Li","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b13","first-page":"12512","article-title":"Kantransformer: Efficient nonlinear transformer via Kolmogorov\u2013Arnold networks","author":"Zhang","year":"2024","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"10.1016\/j.bspc.2026.110698_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123265","article-title":"ESKNet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation","volume":"246","author":"Chen","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110698_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109728","article-title":"Rethinking the unpretentious U-net for medical ultrasound image segmentation","author":"Chen","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110698_b16","series-title":"MaxViT: Multi-axis vision transformer","author":"Tu","year":"2022"},{"key":"10.1016\/j.bspc.2026.110698_b17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13713","article-title":"Coordinate attention for efficient mobile network design","author":"Hou","year":"2021"},{"key":"10.1016\/j.bspc.2026.110698_b18","article-title":"SENetV2: Enhanced squeeze-and-excitation for efficient CNNs","author":"Hou","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110698_b19","doi-asserted-by":"crossref","unstructured":"D. Jha, P.H. Smedsrud, M.A. Riegler, D. Johansen, P. Halvorsen, T. de Lange, ResUNet++: An Advanced Architecture for Medical Image Segmentation, in: IEEE International Symposium on Multimedia, ISM, 2019, pp. 225\u2013230.","DOI":"10.1109\/ISM46123.2019.00049"},{"issue":"10","key":"10.1016\/j.bspc.2026.110698_b20","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110698_b21","doi-asserted-by":"crossref","unstructured":"S. Woo, J. Park, J.-Y. Lee, I.S. Kweon, CBAM: Convolutional Block Attention Module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"2","key":"10.1016\/j.bspc.2026.110698_b22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.bspc.2026.110698_b23","article-title":"MissFormer: An effective transformer for medical image segmentation","volume":"82","author":"Dong","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110698_b24","article-title":"DS-TransUNet: Dual-scale transformer U-net for medical image segmentation","volume":"155","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110698_b25","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"488","article-title":"Nnu-net revisited: A call for rigorous validation in 3D medical image segmentation","volume":"vol. 15049","author":"Isensee","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b26","article-title":"Dual-path learning network for breast ultrasound segmentation","volume":"72","author":"Zhang","year":"2021","journal-title":"Med. Image Anal."},{"issue":"8","key":"10.1016\/j.bspc.2026.110698_b27","first-page":"2035","article-title":"PDD-net: Parallel dense decoding for medical image segmentation","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110698_b28","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"14","article-title":"TransFuse: Fusing transformers and CNNs for medical image segmentation","author":"Zhang","year":"2021"},{"key":"10.1016\/j.bspc.2026.110698_b29","unstructured":"J. Chen, H. Xu, K. Wang, HiFormer: Hierarchical Multi-scale Transformer for Medical Image Segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 1123\u20131133."},{"issue":"9","key":"10.1016\/j.bspc.2026.110698_b30","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","article-title":"H2Former: An efficient hierarchical hybrid transformer for medical image segmentation","volume":"42","author":"He","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110698_b31","series-title":"U-Mamba: Enhancing mamba for medical image segmentation","author":"Ma","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b32","article-title":"ConDSeg: A general medical image segmentation framework via contrast-driven feature enhancement","volume":"vol. 39","author":"Lei","year":"2025"},{"key":"10.1016\/j.bspc.2026.110698_b33","series-title":"Rethinking U-net: Task-adaptive mixture of skip connections for enhanced medical image segmentation","author":"Luo","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b34","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2024","first-page":"123","article-title":"MaxViT-UNet: Multi-axis attention for medical image segmentation","author":"Chen","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.bspc.2026.110698_b36","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","article-title":"Gland segmentation in colon histology images: The GlaS challenge contest","volume":"35","author":"Sirinukunwattana","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110698_b37","first-page":"99","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"vol. 43","author":"Bernal","year":"2015"},{"key":"10.1016\/j.bspc.2026.110698_b38","series-title":"COVID-19 image data collection: Prospective predictions are the future","author":"Cohen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110698_b39","article-title":"FIVES: A fundus image vessel segmentation benchmark dataset","volume":"72","author":"Hu","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110698_b40","article-title":"Directional connectivity-based segmentation of medical images","author":"Yang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110698_b41","series-title":"Rolling-unet: Revitalizing MLP\u2019s ability to efficiently extract long-distance dependencies for medical image segmentation","author":"Liu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110698_b42","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"618","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012528?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012528?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T09:30:44Z","timestamp":1783157444000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426012528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":42,"alternative-id":["S1746809426012528"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110698","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"BiMaxKAN: A KAN-based dual-branch network for medical image segmentation with interpretability analysis","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110698","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110698"}}