{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T21:06:27Z","timestamp":1774299987302,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3663864","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:56:35Z","timestamp":1770843395000},"page":"41835-41851","source":"Crossref","is-referenced-by-count":0,"title":["Implantable Adaptive Cells in U-Net Skip Connections: Low-Compute Retrofit for Medical Segmentation"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-6747","authenticated-orcid":false,"given":"Emil","family":"Benedykciuk","sequence":"first","affiliation":[{"name":"Institute of Computer Science and Mathematics, Maria Curie-Sk&#x0142;odowska University, Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2491-091X","authenticated-orcid":false,"given":"Marcin","family":"Denkowski","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Mathematics, Maria Curie-Sk&#x0142;odowska University, Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4678-9874","authenticated-orcid":false,"given":"Grzegorz","family":"W\u00f3jcik","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Mathematics, Maria Curie-Sk&#x0142;odowska University, Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-11058-w"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref3","article-title":"FasterSeg: Searching for faster real-time semantic segmentation","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01374"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00919-9_12"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908991"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_26"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2019.00035"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00578"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02008"},{"key":"ref11","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. 7th Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3665138"},{"key":"ref13","first-page":"7932","article-title":"Medical neural architecture search: Survey and taxonomy","volume-title":"Proc. IJCAI","author":"Benmeziane"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"ref16","first-page":"1","article-title":"AdwU-net: Adaptive depth and width U-Net for medical image segmentation by differentiable neural architecture search","volume-title":"Proc. 5th Int. Conf. Med. Imag. Deep Learn.","author":"Huang"},{"key":"ref17","article-title":"BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification","volume":"110","author":"Liu","year":"2023","journal-title":"Computerized Med. Imag. Graph."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/EDGE62653.2024.00030"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i4.37292"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127084","article-title":"L-SSHNN: A larger search space of semi-supervised hybrid NAS network for echocardiography segmentation","volume":"276","author":"Chen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref21","article-title":"PCDARTS: Partial channel connections for memory-efficient architecture search","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref23","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv:1804.03999"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-36711-4_13"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2959609"},{"key":"ref26","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"Simpson","year":"2019","journal-title":"arXiv:1902.09063"},{"key":"ref27","article-title":"Development of skip connection in deep neural networks for computer vision and medical image analysis: A survey","author":"Xu","year":"2024","journal-title":"arXiv:2405.01725"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1611.01578"},{"key":"ref32","article-title":"A survey on neural architecture search based on reinforcement learning","author":"Shao","year":"2024","journal-title":"arXiv:2409.18163"},{"key":"ref33","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Real"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3100554"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00046"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111466"},{"key":"ref38","article-title":"Differentiable neural architecture search for medical image segmentation: A systematic review and field audit","volume":"128","author":"Benedykciuk","year":"2026","journal-title":"Computerized Med. Imag. Graph."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_26"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117242","article-title":"HASA: Hybrid architecture search with aggregation strategy for echinococcosis classification and ovary segmentation in ultrasound images","volume":"202","author":"Qian","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102268"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03568-9"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3406065"},{"key":"ref44","volume-title":"Project MONAI, Auto3Dseg Tutorial","year":"2024"},{"key":"ref45","first-page":"10096","article-title":"EfficientNetV2: Smaller models and faster training","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref47","first-page":"7105","article-title":"NAS-bench-101: Towards reproducible neural architecture search","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Ying"},{"key":"ref48","article-title":"NAS-bench-201: Extending the scope of reproducible neural architecture search","volume-title":"Proc. 8th Int. Conf. Learn. Represent. (ICLR)","author":"Dong"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref51","article-title":"Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set]","author":"Bakas","year":"2017"},{"key":"ref52","article-title":"The brain tumor segmentation (BraTS) challenge 2023: Brain MR image synthesis for tumor segmentation (BraSyn)","author":"Li","year":"2023","journal-title":"arXiv:2305.09011"},{"key":"ref53","article-title":"The KiTS21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT","author":"Heller","year":"2023","journal-title":"arXiv:2307.01984"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2661"},{"key":"ref55","article-title":"SharpDARTS: Faster and more accurate differentiable architecture search","author":"Hundt","year":"2019","journal-title":"arXiv:1903.09900"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11393603.pdf?arnumber=11393603","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T20:09:27Z","timestamp":1774296567000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11393603\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3663864","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}