{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:13:10Z","timestamp":1788779590044,"version":"build-2803163510"},"reference-count":47,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate preoperative segmentation of colorectal cancer lymph node metastasis holds significant clinical value for formulating individualized surgical strategies. However, accurate segmentation remains a formidable challenge due to the complex backgrounds, small sizes, irregular shapes, and indistinct boundaries typically exhibited by colorectal cancer lymph nodes. Consequently, this paper proposes the U-Convolutional KANs Attention Net (UCKAN). First, in complex environment, we replace the convolutional layers with the convolutional KAN module, leveraging the learnable non-linear fitting advantage of KAN to enhance the ability to accurately extract the features of small targets. Second, we invented the Global Spatial Multi-scale Attention (GSMA) module, which integrates global context and multi-scale features to improve the model\u2019s sensitivity to small-volume lymph nodes, achieving precise spatial focusing on lesions. Evaluations on practical medical imaging tasks show that the UCKAN model has superior performance., with Dice and IoU scores reaching 91.52 and 83.29\u202f%, respectively, comprehensively outperforming eight advanced medical image segmentation models. The innovative mechanisms proposed in this study effectively address the segmentation challenges associated with low-contrast and multi-scale lesions, providing a reliable quantitative basis for formulating individualized preoperative surgical strategies and demonstrating significant potential for clinical translation.<\/jats:p>","DOI":"10.1515\/med-2026-1469","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:56:43Z","timestamp":1788778603000},"source":"Crossref","is-referenced-by-count":0,"title":["Automated segmentation of lymph node metastases in colorectal cancer based on convolutional KAN"],"prefix":"10.1515","volume":"21","author":[{"given":"Peng","family":"Wang","sequence":"first","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University , Daqing , Heilongjiang , China"},{"name":"Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University , Daqing , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixiong","family":"Fan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University , Daqing , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2891-6993","authenticated-orcid":false,"given":"Haoyu","family":"Wu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University , Daqing , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Admissions and Employment Office, Northeast Petroleum University , Daqing , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyi","family":"Lu","sequence":"additional","affiliation":[{"name":"SANYA Offshore Oil & Gas Research lnstitute, Northeast Petroleum University , Sanyan , Hainan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5296-220X","authenticated-orcid":false,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harbin Medical University Cancer Hospital , Harbin , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0380-6607","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harbin Medical University Cancer Hospital , Harbin , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0824-5372","authenticated-orcid":false,"given":"Baoshun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Colorectal Surgery Department, Longnan Hospital of Daqing , Daqing , Heilongjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2026090710563778802_j_med-2026-1469_ref_001","doi-asserted-by":"crossref","unstructured":"Itatani, Y, Kawada, K, Hida, K, Deguchi, Y, Oshima, N, Mizuno, R, et al.. Laparoscopic left hemicolectomy with regional lymph node navigation and intracorporeal anastomosis for splenic flexure colon cancer. Int Cancer Conf J 2020;9:170\u20134. https:\/\/doi.org\/10.1007\/s13691-020-00424-4.","DOI":"10.1007\/s13691-020-00424-4"},{"key":"2026090710563778802_j_med-2026-1469_ref_002","doi-asserted-by":"crossref","unstructured":"Kim, HJ, Choi, GS. Clinical implications of lymph node metastasis in colorectal cancer: current status and future perspectives. Ann Coloproctol 2019;35:109\u201317. https:\/\/doi.org\/10.3393\/ac.2019.06.12.","DOI":"10.3393\/ac.2019.06.12"},{"key":"2026090710563778802_j_med-2026-1469_ref_003","doi-asserted-by":"crossref","unstructured":"Diefenhardt, M, Martin, D, Hofheinz, R-D, Ghadimi, M, Fokas, E, R\u00f6del, C, et al.. Persistent lymph node metastases after neoadjuvant chemoradiotherapy for rectal cancer. JAMA 2024;7:e2432927\u20137. https:\/\/doi.org\/10.1001\/jamanetworkopen.2024.32927.","DOI":"10.1001\/jamanetworkopen.2024.32927"},{"key":"2026090710563778802_j_med-2026-1469_ref_004","doi-asserted-by":"crossref","unstructured":"Miyashita, S, Shimizu, T, Ishizuka, M, Niki, M, Nishi, Y, Shibuya, N, et al.. N2 lymph node metastasis is a useful predictor of recurrence in patients with stage III rectal cancer undergoing adjuvant chemotherapy using tegafur\u2013uracil\/leucovorin. Anticancer Res 2023;43:2219\u201325. https:\/\/doi.org\/10.21873\/anticanres.16385.","DOI":"10.21873\/anticanres.16385"},{"key":"2026090710563778802_j_med-2026-1469_ref_005","doi-asserted-by":"crossref","unstructured":"A Salega, M M\u00fcnch, P Renner, K-P Thon, Steurer, W, M\u00f6nch, D, et al.. Late local recurrence after neoadjuvant therapy and radical resection for locally advanced rectal cancer. Cancer (Basel) 2024; 16:448. https:\/\/doi.org\/10.3390\/cancers16020448.","DOI":"10.3390\/cancers16020448"},{"key":"2026090710563778802_j_med-2026-1469_ref_006","doi-asserted-by":"crossref","unstructured":"Modest, D, Pant, S, Sartore-Bianchi, A. Treatment sequencing in metastatic colorectal cancer. Eur J Cancer 2019;109:70\u201383. https:\/\/doi.org\/10.1016\/j.ejca.2018.12.019.","DOI":"10.1016\/j.ejca.2018.12.019"},{"key":"2026090710563778802_j_med-2026-1469_ref_007","doi-asserted-by":"crossref","unstructured":"Bray, F, Laversanne, M, Sung, H, Ferlay, J, Siegel, RL, Soerjomataram, I, et al.. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 2024;74:229\u201363. https:\/\/doi.org\/10.3322\/caac.21834.","DOI":"10.3322\/caac.21834"},{"key":"2026090710563778802_j_med-2026-1469_ref_008","doi-asserted-by":"crossref","unstructured":"Keller, DS, Berho, M, Perez, RO, Wexner, SD, Chand, M. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol 2020;17:414\u201329. https:\/\/doi.org\/10.1038\/s41575-020-0275-y.","DOI":"10.1038\/s41575-020-0275-y"},{"key":"2026090710563778802_j_med-2026-1469_ref_009","doi-asserted-by":"crossref","unstructured":"Hu, J, Chen, S, Pan, Z, Zeng, S, Yang, W. Perspective+ Unet: enhancing segmentation with bi-path fusion and efficient non-local attention for superior receptive fields. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland; 2024:499\u2013509 pp.","DOI":"10.1007\/978-3-031-72114-4_48"},{"key":"2026090710563778802_j_med-2026-1469_ref_010","doi-asserted-by":"crossref","unstructured":"Peng, W, Qiao, H, Mo, L, Guo, YJFi. O. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023;13:1167289. https:\/\/doi.org\/10.3389\/fonc.2023.1167289.","DOI":"10.3389\/fonc.2023.1167289"},{"key":"2026090710563778802_j_med-2026-1469_ref_011","doi-asserted-by":"crossref","unstructured":"Xian, M-F, Zheng, X, Xu, J-B, Li, X, Chen, LD, Wang, W. Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI. Diagn Interv Radiol 2021;27:424\u201331. https:\/\/doi.org\/10.5152\/dir.2021.20031.","DOI":"10.5152\/dir.2021.20031"},{"key":"2026090710563778802_j_med-2026-1469_ref_012","doi-asserted-by":"crossref","unstructured":"Wang, Z, Chen, X, Liu, B, Hai, J, Qiao, K, Yuan, Z, et al.. Deep-learning-based computer-aided grading of cervical spinal stenosis from MR images: accuracy and clinical alignment. Bioengineering 2025;12:604. https:\/\/doi.org\/10.3390\/bioengineering12060604.","DOI":"10.3390\/bioengineering12060604"},{"key":"2026090710563778802_j_med-2026-1469_ref_013","doi-asserted-by":"crossref","unstructured":"Michelutti, L, Tel, A, Robiony, M, Marini, L, Tognetto, D, Agosti, E, et al.. Updates, applications and future directions of deep learning for the images processing in the field of cranio-maxillo-facial surgery. Bioengineering 2025;12:585. https:\/\/doi.org\/10.3390\/bioengineering12060585.","DOI":"10.3390\/bioengineering12060585"},{"key":"2026090710563778802_j_med-2026-1469_ref_014","doi-asserted-by":"crossref","unstructured":"Al-Absi, AA, Fu, R, Ebrahim, N, Al-Absi, MA, Kang, DK. Brain tumour segmentation and grading using local and global context-aggregated attention network architecture. Bioengineering 2025;12:552. https:\/\/doi.org\/10.3390\/bioengineering12050552.","DOI":"10.3390\/bioengineering12050552"},{"key":"2026090710563778802_j_med-2026-1469_ref_015","unstructured":"Zhang, H, Guo, W, Qiu, C, Wan, S, Zou, B, Wang, W, et al.. Care: a large scale ct image dataset and clinical applicable benchmark model for rectal cancer segmentation. arXiv preprint arXiv:2308.08283; 2023."},{"key":"2026090710563778802_j_med-2026-1469_ref_016","doi-asserted-by":"crossref","unstructured":"Liu, J, Yildirim, O, Akin, O, Tian, YJB. AI-driven robust kidney and renal mass segmentation and classification on 3D CT images. Bioengineering 2023;10:116. https:\/\/doi.org\/10.3390\/bioengineering10010116.","DOI":"10.3390\/bioengineering10010116"},{"key":"2026090710563778802_j_med-2026-1469_ref_017","doi-asserted-by":"crossref","unstructured":"Huang, Z, Yang, X, Huang, S, Guo, LJB. SECP-Net: SE-connection pyramid network for segmentation of organs at risk with nasopharyngeal carcinoma. Bioengineering 2023;10:1119. https:\/\/doi.org\/10.3390\/bioengineering10101119.","DOI":"10.3390\/bioengineering10101119"},{"key":"2026090710563778802_j_med-2026-1469_ref_018","doi-asserted-by":"crossref","unstructured":"Liu, X, Qu, L, Xie, Z, Zhao, J, Shi, Y, Song, Z. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. BioMedical Engineering OnLine 2024;23:52. https:\/\/doi.org\/10.1186\/s12938-024-01238-8.","DOI":"10.1186\/s12938-024-01238-8"},{"key":"2026090710563778802_j_med-2026-1469_ref_019","doi-asserted-by":"crossref","unstructured":"J Kaur, B Kaur. A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques. Comput Biol Med 2024;179:108910. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108910.","DOI":"10.1016\/j.compbiomed.2024.108910"},{"key":"2026090710563778802_j_med-2026-1469_ref_020","doi-asserted-by":"crossref","unstructured":"Iqbal, S, Khan, TM, Naqvi, SS, Naveed, A, Meijering, E. TBConvL-Net: a hybrid deep learning architecture for robust medical image segmentation. Pattern Recogn 2025;158:111028. https:\/\/doi.org\/10.1016\/j.patcog.2024.111028.","DOI":"10.1016\/j.patcog.2024.111028"},{"key":"2026090710563778802_j_med-2026-1469_ref_021","doi-asserted-by":"crossref","unstructured":"Ahsan, R, Shahzadi, I, Najeeb, F, Omer, H. Brain tumor detection and segmentation using deep learning. MAGMA 2025;38:13\u201322. https:\/\/doi.org\/10.1007\/s10334-024-01203-5.","DOI":"10.1007\/s10334-024-01203-5"},{"key":"2026090710563778802_j_med-2026-1469_ref_022","doi-asserted-by":"crossref","unstructured":"Y Fu, J Liu, J Shi. TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images. Comput Biol Med 2024;170:107938. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107938, 2024.","DOI":"10.1016\/j.compbiomed.2024.107938"},{"key":"2026090710563778802_j_med-2026-1469_ref_023","doi-asserted-by":"crossref","unstructured":"Shang, H, Tong, Y, Li, M, Xu, S, Cao, Z. Deep learning-based U-Mamba model to predict differentiated gastric cancer using radiomics features from spleen segmentation. Curr Med Imag 2024;20:e15734056349216. https:\/\/doi.org\/10.2174\/0115734056349216241118115005.","DOI":"10.2174\/0115734056349216241118115005"},{"key":"2026090710563778802_j_med-2026-1469_ref_024","doi-asserted-by":"crossref","unstructured":"Li, C, Sultan, RI, Khanduri, P, Qiang, Y, Indrin, C, Zhu, D.: automated prompting SAM for 3D multi-organ segmentation. In: Proceedings of the Winter Conference on Applications of Computer Vision. Tucson, AZ, USA: IEEE; 2025:3570\u201380 pp.","DOI":"10.1109\/WACV61041.2025.00352"},{"key":"2026090710563778802_j_med-2026-1469_ref_025","doi-asserted-by":"crossref","unstructured":"Yang, C, Qin, LH, Xie, YE, Liao, JY. Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis. Radiat Oncol 2022;17:175. https:\/\/doi.org\/10.1186\/s13014-022-02148-6.","DOI":"10.1186\/s13014-022-02148-6"},{"key":"2026090710563778802_j_med-2026-1469_ref_026","doi-asserted-by":"crossref","unstructured":"Bouget, D, Pedersen, A, Vanel, J, Leira, HO, Lang\u00f8, T. Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding. Comput Methods Biomech Biomed Eng 2023;11:44\u201358. https:\/\/doi.org\/10.1080\/21681163.2022.2043778.","DOI":"10.1080\/21681163.2022.2043778"},{"key":"2026090710563778802_j_med-2026-1469_ref_027","doi-asserted-by":"crossref","unstructured":"Strijbis, VI, Dahele, M, Gurney-Champion, OJ, Blom, GJ, Vergeer, MR, Slotman, BJ, et al.. Deep learning for automated elective lymph node level segmentation for head and neck cancer radiotherapy. Cancers 2022;14:5501. https:\/\/doi.org\/10.3390\/cancers14225501.","DOI":"10.3390\/cancers14225501"},{"key":"2026090710563778802_j_med-2026-1469_ref_028","doi-asserted-by":"crossref","unstructured":"Guo, W, Zhang, H, Wan, S, Zou, B, Wang, W, Qiu, C, et al.. Meply: a large-scale dataset and baseline evaluations for metastatic perirectal lymph node detection and segmentation. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Singapore: Springer Nature Singapore; 2024:358\u201370 pp.","DOI":"10.1007\/978-981-97-8496-7_25"},{"key":"2026090710563778802_j_med-2026-1469_ref_029","doi-asserted-by":"crossref","unstructured":"Purwono, P, Wulandari, ANE, Nisa, KJAM, Systems, M. Explainable artificial intelligence (XAI) in medical imaging: techniques, applications, challenges, and future directions. Adv Mech Mechatron Syst 2025;1:52\u201366. https:\/\/doi.org\/10.53623\/amms.v1i1.692.","DOI":"10.53623\/amms.v1i1.692"},{"key":"2026090710563778802_j_med-2026-1469_ref_030","doi-asserted-by":"crossref","unstructured":"Toumaj, S, Heidari, A, Navimipour, NJ. Leveraging explainable artificial intelligence for transparent and trustworthy cancer detection systems. Artif Intell Med 2025;169:103243. https:\/\/doi.org\/10.1016\/j.artmed.2025.103243.","DOI":"10.1016\/j.artmed.2025.103243"},{"key":"2026090710563778802_j_med-2026-1469_ref_031","unstructured":"Liu, Z, Wang, Y, Vaidya, S, Ruehle, F, Halverson, J, Soljacic, M, et al.. KAN: Kolmogorov\u2013Arnold networks. In: International Conference on Learning Representations. Singapore: ICLR Proceedings; 2025:70367\u2013413 pp."},{"key":"2026090710563778802_j_med-2026-1469_ref_032","unstructured":"Bodner, AD, Tepsich, AS, Spolski, JN, Pourteau, SJ. Convolutional Kolmogorov\u2013Arnold networks. arXiv preprint arXiv:2406.13155; 2024."},{"key":"2026090710563778802_j_med-2026-1469_ref_033","doi-asserted-by":"crossref","unstructured":"Ronneberger, O, Fischer, P, Brox, T. U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing; 2015:234\u201341 pp.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2026090710563778802_j_med-2026-1469_ref_034","doi-asserted-by":"crossref","unstructured":"Suzuki, K, Iwahori, Y, Funahashi, K, Bhuyan, MK, Ouchi, A, Shimizu, Y. Lymph-node detection and metastasis classification from CT images\u00a0using a single U-Net model. Procedia Comput Sci 2022;207:583\u201392. https:\/\/doi.org\/10.1016\/j.procs.2022.09.113.","DOI":"10.1016\/j.procs.2022.09.113"},{"key":"2026090710563778802_j_med-2026-1469_ref_035","doi-asserted-by":"crossref","unstructured":"J Qiu, C Chen, M Li, J Hong, Dong, B, Xu, S, et al.. Attention-enhanced residual U-Net: lymph node segmentation method with bimodal MRI images. Biomed Eng Online 2025;24: 67. https:\/\/doi.org\/10.1186\/s12938-025-01400-w.","DOI":"10.1186\/s12938-025-01400-w"},{"key":"2026090710563778802_j_med-2026-1469_ref_036","doi-asserted-by":"crossref","unstructured":"Manjunatha, Y, Sharma, V, Iwahori, Y, Bhuyan, MK, Wang, A, Ouchi, A, et al.. Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network. Int J Comput Assist Radiol Surg 2023;18:723\u201332. https:\/\/doi.org\/10.1007\/s11548-022-02822-w.","DOI":"10.1007\/s11548-022-02822-w"},{"key":"2026090710563778802_j_med-2026-1469_ref_037","doi-asserted-by":"crossref","unstructured":"Zhou, Q, Zhou, Y, Hou, N, Zhang, Y, Zhu, G, Li, L. DFA-UNet: dual-stream feature-fusion attention U-Net for lymph node segmentation in lung cancer diagnosis. Front Neurosci 2024;18:1448294. https:\/\/doi.org\/10.3389\/fnins.2024.1448294.","DOI":"10.3389\/fnins.2024.1448294"},{"key":"2026090710563778802_j_med-2026-1469_ref_038","unstructured":"Gu, A, Dao, T. Mamba: linear-time sequence modeling with selective state spaces. First conference on language modeling. arXiv preprint arXiv:2312.00752; 2024."},{"key":"2026090710563778802_j_med-2026-1469_ref_039","doi-asserted-by":"crossref","unstructured":"Du, Q, Wang, L, Chen, HJSR. A mixed mamba U-Net for prostate segmentation in MR images. Sci Rep 2024;14:19976. https:\/\/doi.org\/10.1038\/s41598-024-71045-7.","DOI":"10.1038\/s41598-024-71045-7"},{"key":"2026090710563778802_j_med-2026-1469_ref_040","doi-asserted-by":"crossref","unstructured":"Abulizi, A, Wang, M, Abudukelimu, M, Gao, Y, Shi, Y, Aizizi, M, et al.. A U-shaped network with multi-branch convolutions and Mamba integration for pulmonary nodule segmentation. Biomed Signal Process Control 2026;112:108606. https:\/\/doi.org\/10.1016\/j.bspc.2025.108606.","DOI":"10.1016\/j.bspc.2025.108606"},{"key":"2026090710563778802_j_med-2026-1469_ref_041","doi-asserted-by":"crossref","unstructured":"Jiang, F, Zhong, Y, Yang, SJDH. TUMamba: a novel tongue segment methods based on Mamba and U-Net. Digit Health 2024;10:20552076241289007. https:\/\/doi.org\/10.1177\/20552076241289007.","DOI":"10.1177\/20552076241289007"},{"key":"2026090710563778802_j_med-2026-1469_ref_042","doi-asserted-by":"crossref","unstructured":"Ju, M, Xie, S, Li, FJ. Improving skip connection in U-Net through fusion perspective with mamba for image dehazing. IEEE Trans Consum Electron 2024;70:7505\u201314. https:\/\/doi.org\/10.1109\/tce.2024.3417476.","DOI":"10.1109\/TCE.2024.3417476"},{"key":"2026090710563778802_j_med-2026-1469_ref_043","doi-asserted-by":"crossref","unstructured":"Bian, C, Chen, X. Mambaclinix: hierarchical gated convolution and mamba-based U-Net for enhanced 3d medical image segmentation. Biomed Signal Process Control 2026;124:110657.","DOI":"10.1016\/j.bspc.2026.110657"},{"key":"2026090710563778802_j_med-2026-1469_ref_044","doi-asserted-by":"crossref","unstructured":"Tang, T, Yan, W, Bai, G, Pan, X, Liu, J. UVMSR: a novel approach to hyperspectral image super-resolution by fusing U-Net and Mamba. Int J Rem Sens 2025;46:2023\u201354. https:\/\/doi.org\/10.1080\/01431161.2024.2443619.","DOI":"10.1080\/01431161.2024.2443619"},{"key":"2026090710563778802_j_med-2026-1469_ref_045","unstructured":"Penkin, M, Krylov, AJ. FunKAN: functional Kolmogorov-Arnold network for medical image enhancement and segmentation. arXiv preprint arXiv:2509.13508; 2025."},{"key":"2026090710563778802_j_med-2026-1469_ref_046","doi-asserted-by":"crossref","unstructured":"Li, C, Liu, X, Li, W, Wang, C, Liu, H, Liu, Y, et al.. U-kan makes strong backbone for medical image segmentation and generation. Proc AAAI Conf Artif Intell 2025;39:4652\u201360. https:\/\/doi.org\/10.1609\/aaai.v39i5.32491.","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"2026090710563778802_j_med-2026-1469_ref_047","doi-asserted-by":"crossref","unstructured":"Yang, Z, Zhang, J, Luo, X, Wu, X, Lu, Z, Shen, L.: an advanced Kolmogorov-Arnold network for medical image classification. In: 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Wuhan, China: IEEE; 2025:3090\u20137.","DOI":"10.1109\/BIBM66473.2025.11356561"}],"container-title":["Open Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/med-2026-1469\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/med-2026-1469\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:57:12Z","timestamp":1788778632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/med-2026-1469\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,9,7]]},"published-print":{"date-parts":[[2026,1,23]]}},"alternative-id":["10.1515\/med-2026-1469"],"URL":"https:\/\/doi.org\/10.1515\/med-2026-1469","relation":{},"ISSN":["2391-5463"],"issn-type":[{"value":"2391-5463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"article-number":"20261469"}}