{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T09:19:21Z","timestamp":1792574361633,"version":"4.5.0"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate segmentation of nuclei in histopathological images is critical for improving diagnostic precision and advancing computational pathology. Deep learning models employed for this task must effectively handle structural variability while offering transparent and interpretable predictions to ensure clinical reliability. In this study, we investigate the integration of Kolmogorov\u2013Arnold Networks (KANs) into the widely adopted U\u2010Net architecture, forming a novel hybrid model referred to as U\u2010KAN. To the best of our knowledge, we are the first to explore the application of U\u2010KAN for multi\u2010class nuclei segmentation on the challenging MoNuSAC2020 dataset, leveraging an adaptive sliding window strategy. Our results demonstrate that U\u2010KAN achieves a 17.9% improvement in Dice coefficient (Dice Similarity Coefficient, DSC) (0.976) and a 25.7% increase in IoU (Intersection over Union) (0.954) compared to baseline method (U\u2010Net), while also delivering enhanced model interpretability. Gradient\u2010based explanation techniques further confirm that U\u2010KAN produces anatomically plausible predictions, with strong attention to nuclear boundaries. These findings suggest that symbolic\u2010connectionist hybrids like U\u2010KAN can meaningfully advance automated histopathological image analysis.<\/jats:p>","DOI":"10.1002\/ima.70283","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T04:32:07Z","timestamp":1767760327000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["U\u2010\n                    <scp>KAN<\/scp>\n                    for Multi\u2010Nuclei Segmentation Using an Adaptive Sliding Window Approach"],"prefix":"10.1002","volume":"36","author":[{"given":"Usman","family":"Ali","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Qingshuihe Campus  Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Qingshuihe Campus  Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9907-5085","authenticated-orcid":false,"given":"Aiman","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering University of Florence (UniFI)  Florence Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Hammad","family":"Musaddiq","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Qingshuihe Campus  Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/1066896913517939"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41698\u2010017\u20100022\u20101"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11020339"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14051199"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010019\u20100052\u20101"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0114885"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00412\u2010016\u20100614\u20105"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2677499"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859\u2010017\u20101934\u2010z"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/gb\u20102006\u20107\u201010\u2010r100"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2947628"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3085712"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcb.2021.12.004"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101771"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.2174\/1573405617666210127154257"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.3233\/XST\u2010200715"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.33093\/ijoras.2024.6.1.12"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108829"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104649"},{"key":"e_1_2_13_22_1","unstructured":"C.Wang A.Mahbod I.Ellinger et\u00a0al. \u201cFUSeg: The Foot Ulcer Segmentation Challenge \u201d arXiv:2201.00414v1(2022) https:\/\/doi.org\/10.48550\/arXiv.2201.00414."},{"key":"e_1_2_13_23_1","first-page":"234","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention \u2013 MICCAI 2015. 9351 of Lecture Notes in Computer Science","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896920"},{"key":"e_1_2_13_25_1","first-page":"75","volume-title":"15th European Congress on Digital Pathology (ECDP 2019). 11435 of Lecture Notes in Computer Science","author":"Mahbod A.","year":"2019"},{"key":"e_1_2_13_26_1","first-page":"208","volume-title":"2019 IEEE 16th International Symposium on Biomedical Imaging","author":"Vuola A. 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