{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T12:08:00Z","timestamp":1779970080861,"version":"3.53.1"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.displa.2026.103533","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T17:58:15Z","timestamp":1779299895000},"page":"103533","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["PolypLGLF: Layer-group feature extraction with low-coupling fusion network for polyp segmentation"],"prefix":"10.1016","volume":"95","author":[{"given":"Weixiong","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangzu","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liman","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-6426","authenticated-orcid":false,"given":"Cunlu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103533_b1","series-title":"Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000\u20132019","first-page":"48","author":"Organization","year":"2020"},{"issue":"20","key":"10.1016\/j.displa.2026.103533_b2","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.3390\/diagnostics13203199","article-title":"Tumor progression from a fibroblast activation protein perspective: Novel diagnostic and therapeutic scenarios for colorectal cancer","volume":"13","author":"Rossetti","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.displa.2026.103533_b3","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference","first-page":"253","article-title":"Adaptive context selection for polyp segmentation","volume":"Vol. 12266","author":"Zhang","year":"2020"},{"key":"10.1016\/j.displa.2026.103533_b4","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3748\/wjg.v25.i2.190","article-title":"Adverse events related to colonoscopy: Global trends and future challenges","volume":"25","author":"Kim","year":"2019","journal-title":"World J. Gastroenterol."},{"key":"10.1016\/j.displa.2026.103533_b5","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"892","article-title":"FCN-transformer feature fusion for polyp segmentation","author":"Sanderson","year":"2022"},{"key":"10.1016\/j.displa.2026.103533_b6","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference","first-page":"120","article-title":"Automatic polyp segmentation via multi-scale subtraction network","author":"Zhao","year":"2021"},{"key":"10.1016\/j.displa.2026.103533_b7","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises","volume":"109","author":"Zhou","year":"2020","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.displa.2026.103533_b8","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","article-title":"Automated polyp detection in colonoscopy videos using shape and context information","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.displa.2026.103533_b9","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/jimaging6070069","article-title":"Polyp segmentation with fully convolutional deep neural networks\u2014extended evaluation study","volume":"6","author":"Guo","year":"2020","journal-title":"J. Imaging"},{"key":"10.1016\/j.displa.2026.103533_b10","doi-asserted-by":"crossref","unstructured":"B. Sushma, C. Raghavendra, J. Prashanth, CNN based U-Net with Modified Skip Connections for Colon Polyp Segmentation, in: 2021 5th International Conference on Computing Methodologies and Communication, ICCMC, 2021, pp. 1762\u20131766.","DOI":"10.1109\/ICCMC51019.2021.9418037"},{"key":"10.1016\/j.displa.2026.103533_b11","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference","first-page":"263","article-title":"Pranet: Parallel reverse attention network for polyp segmentation","author":"Fan","year":"2020"},{"key":"10.1016\/j.displa.2026.103533_b12","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.displa.2026.103533_b13","doi-asserted-by":"crossref","unstructured":"Z. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, J. Liang, UNet++: A Nested U-Net Architecture for Medical Image Segmentation, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S..., Vol. 11045, 2018, pp. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"10.1016\/j.displa.2026.103533_b14","series-title":"HarDNet-MSEG: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS","author":"Huang","year":"2021"},{"key":"10.1016\/j.displa.2026.103533_b15","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference","first-page":"302","article-title":"Selective feature aggregation network with area-boundary constraints for polyp segmentation","author":"Fang","year":"2019"},{"key":"10.1016\/j.displa.2026.103533_b16","doi-asserted-by":"crossref","unstructured":"K. Patel, A.M. Bur, G. Wang, Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation, in: 2021 18th Conference on Robots and Vision, CRV, 2021, pp. 181\u2013188.","DOI":"10.1109\/CRV52889.2021.00032"},{"key":"10.1016\/j.displa.2026.103533_b17","series-title":"Medical Imaging with Deep Learning","first-page":"1526","article-title":"Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image segmentation","author":"Rahman","year":"2024"},{"key":"10.1016\/j.displa.2026.103533_b18","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.displa.2026.103533_b19","article-title":"Polyp-PVT: Polyp segmentation with pyramid vision transformers","volume":"2","author":"Dong","year":"2023","journal-title":"CAAI Artif. Intell. Res."},{"key":"10.1016\/j.displa.2026.103533_b20","doi-asserted-by":"crossref","first-page":"80575","DOI":"10.1109\/ACCESS.2022.3195241","article-title":"ColonFormer: An efficient transformer based method for colon polyp segmentation","volume":"10","author":"Duc","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.displa.2026.103533_b21","series-title":"LAPFormer: A light and accurate polyp segmentation transformer","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.displa.2026.103533_b22","series-title":"M2SNet: Multi-scale in multi-scale subtraction network for medical image segmentation","author":"Zhao","year":"2023"},{"key":"10.1016\/j.displa.2026.103533_b23","article-title":"Multi-level feature fusion network combining attention mechanisms for polyp segmentation","volume":"104","author":"Liu","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.displa.2026.103533_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108824","article-title":"MIA-Net: Multi-information aggregation network combining transformers and convolutional feature learning for polyp segmentation","volume":"247","author":"Li","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.displa.2026.103533_b25","doi-asserted-by":"crossref","unstructured":"N.-T. Bui, D.-H. Hoang, Q.-T. Nguyen, M.-T. Tran, N. Le, MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation, in: 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV, 2024, pp. 7985\u20137994.","DOI":"10.1109\/WACV57701.2024.00780"},{"key":"10.1016\/j.displa.2026.103533_b26","doi-asserted-by":"crossref","unstructured":"Y. Zhao, C. Luo, Z.J. Zha, et al., Multi-scale group transformer for long sequence modeling in speech separation, in: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, pp. 3251\u20133257.","DOI":"10.24963\/ijcai.2020\/450"},{"issue":"2","key":"10.1016\/j.displa.2026.103533_b27","first-page":"616","article-title":"SwinE-Net: Hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer","volume":"9","author":"Park","year":"2022","journal-title":"J. Comput. Des. Eng."},{"key":"10.1016\/j.displa.2026.103533_b28","doi-asserted-by":"crossref","unstructured":"T.H. Nguyen-Mau, T.V. Hoang, H.D. Nguyen, et al., ConvTransNet: Merging Convolution with Transformer to Enhance Polyp Segmentation, in: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, pp. 631\u2013638.","DOI":"10.1145\/3628797.3629014"},{"key":"10.1016\/j.displa.2026.103533_b29","series-title":"Cooperation learning enhanced colonic polyp segmentation based on transformer-CNN fusion","author":"Wang","year":"2023"},{"key":"10.1016\/j.displa.2026.103533_b30","series-title":"MugenNet: A novel combined convolution neural network and transformer network with its application for colonic polyp image segmentation","author":"Peng","year":"2024"},{"key":"10.1016\/j.displa.2026.103533_b31","doi-asserted-by":"crossref","unstructured":"Y. Zhang, H. Liu, Q. Hu, TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021, pp. 14\u201324.","DOI":"10.1007\/978-3-030-87193-2_2"},{"issue":"8","key":"10.1016\/j.displa.2026.103533_b32","doi-asserted-by":"crossref","first-page":"9454","DOI":"10.1109\/TPAMI.2023.3243048","article-title":"Conformer: Local features coupling global representations for recognition and detection","volume":"45","author":"Peng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103533_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107760","article-title":"Dual-branch multi-information aggregation network with transformer and convolution for polyp segmentation","volume":"168","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.displa.2026.103533_b34","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"592","article-title":"FocalUNETR: A focal transformer for boundary-aware prostate segmentation using CT images","author":"Li","year":"2023"},{"key":"10.1016\/j.displa.2026.103533_b35","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"4655","article-title":"SAM-Mamba: Mamba guided SAM architecture for generalized zero-shot polyp segmentation","author":"Dutta","year":"2025"},{"key":"10.1016\/j.displa.2026.103533_b36","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025","first-page":"455","article-title":"PolyMamba: Spatial-prior guided mamba for polyp segmentation with high-frequency enhancement","volume":"Vol. LNCS 15970","author":"Fu","year":"2025"},{"key":"10.1016\/j.displa.2026.103533_b37","series-title":"2025 IEEE\/CVF International Conference on Computer Vision Workshops","first-page":"1093","article-title":"BAMPolyp: Bi-axial mamba bottleneck for gastrointestinal polyp segmentation","author":"Islam","year":"2025"},{"issue":"7","key":"10.1016\/j.displa.2026.103533_b38","doi-asserted-by":"crossref","first-page":"3785","DOI":"10.3390\/app15073785","article-title":"Medical image segmentation network based on dual-encoder interactive fusion","volume":"15","author":"Yang","year":"2025","journal-title":"Appl. Sci."},{"issue":"20","key":"10.1016\/j.displa.2026.103533_b39","doi-asserted-by":"crossref","first-page":"6495","DOI":"10.3390\/s25206495","article-title":"Research on polyp segmentation via dynamic multi-scale feature fusion and global\u2013local semantic enhancement","volume":"25","author":"Qing","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.displa.2026.103533_b40","doi-asserted-by":"crossref","unstructured":"D. Jha, P.H. Smedsrud, M. Riegler, D. Johansen, T. de Lange, P. Halvorsen, H.D. Johansen, ResUNet++: An Advanced Architecture for Medical Image Segmentation, in: 2019 IEEE International Symposium on Multimedia, ISM, 2019, pp. 225\u20132255.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"10.1016\/j.displa.2026.103533_b41","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference","first-page":"699","article-title":"Shallow attention network for polyp segmentation","author":"Wei","year":"2021"},{"key":"10.1016\/j.displa.2026.103533_b42","doi-asserted-by":"crossref","unstructured":"W. Wang, E. Xie, X. Li, et al., Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.displa.2026.103533_b43","doi-asserted-by":"crossref","unstructured":"J.M.J. Valanarasu, V.M. Patel, UNeXt: Mlp-Based Rapid Medical Image Segmentation Network, in: 25th International Conference of Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022, Singapore, 2022, pp. 23\u201333.","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"10.1016\/j.displa.2026.103533_b44","doi-asserted-by":"crossref","unstructured":"X. Dong, J. Bao, D. Chen, et al., CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows, in: Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 2022, pp. 12114\u201312124.","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"10.1016\/j.displa.2026.103533_b45","series-title":"HRFormer: High-resolution transformer for dense prediction","author":"Yuan","year":"2021"},{"key":"10.1016\/j.displa.2026.103533_b46","article-title":"Multi-scale nested UNet with transformer for colorectal polyp segmentation","author":"Wang","year":"2024","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.displa.2026.103533_b47","doi-asserted-by":"crossref","unstructured":"Z. Peng, W. Huang, S. Gu, et al., Conformer: Local features coupling global representations for visual recognition, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 367\u2013376.","DOI":"10.1109\/ICCV48922.2021.00042"},{"issue":"17","key":"10.1016\/j.displa.2026.103533_b48","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acede8","article-title":"CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation","volume":"68","author":"Chen","year":"2023","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"10.1016\/j.displa.2026.103533_b49","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.3390\/app14104073","article-title":"Attention-based two-branch hybrid fusion network for medical image segmentation","volume":"14","author":"Liu","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.displa.2026.103533_b50","series-title":"ParaTransCNN: Parallelized TransCNN encoder for medical image segmentation","author":"Sun","year":"2024"},{"issue":"8","key":"10.1016\/j.displa.2026.103533_b51","doi-asserted-by":"crossref","first-page":"4090","DOI":"10.1109\/JBHI.2022.3173948","article-title":"Boundary constraint network with cross layer feature integration for polyp segmentation","volume":"26","author":"Yue","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"16","key":"10.1016\/j.displa.2026.103533_b52","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.3390\/electronics11162501","article-title":"A segmentation algorithm of colonoscopy images based on multi-scale feature fusion","volume":"11","author":"Yu","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.displa.2026.103533_b53","doi-asserted-by":"crossref","unstructured":"T. Kim, H. Lee, D. Kim, Uacanet: Uncertainty augmented context attention for polyp segmentation, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 2167\u20132175.","DOI":"10.1145\/3474085.3475375"},{"key":"10.1016\/j.displa.2026.103533_b54","doi-asserted-by":"crossref","unstructured":"M.M. Rahman, R. Marculescu, Medical image segmentation via cascaded attention decoding, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 6222\u20136231.","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"10.1016\/j.displa.2026.103533_b55","series-title":"Chinese Conference on Pattern Recognition and Computer Vision","first-page":"343","article-title":"DuAT: Dual-aggregation transformer network for medical image segmentation","author":"Tang","year":"2023"},{"key":"10.1016\/j.displa.2026.103533_b56","series-title":"MultiMedia Modeling: 26th International Conference","first-page":"451","article-title":"Kvasir-seg: A segmented polyp dataset","author":"Jha","year":"2020"},{"key":"10.1016\/j.displa.2026.103533_b57","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph. : Off. J. Comput. Med. Imaging Soc."},{"issue":"1","key":"10.1016\/j.displa.2026.103533_b58","article-title":"A benchmark for endoluminal scene segmentation of colonoscopy images","volume":"2017","author":"V\u00e1zquez","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"10.1016\/j.displa.2026.103533_b59","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","article-title":"Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.displa.2026.103533_b60","doi-asserted-by":"crossref","unstructured":"R. Margolin, L. Zelnik-Manor, A. Tal, How to evaluate foreground maps?, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2014.39"},{"key":"10.1016\/j.displa.2026.103533_b61","doi-asserted-by":"crossref","unstructured":"F. Perazzi, P. Kr\u00e4henb\u00fchl, Y. Pritch, A. Sorkine-Hornung, Saliency filters: Contrast based filtering for salient region detection, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 733\u2013740.","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"10.1016\/j.displa.2026.103533_b62","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.displa.2026.103533_b63","doi-asserted-by":"crossref","unstructured":"J. Wei, S. Wang, Q. Huang, F3Net: Fusion, Feedback and Focus for Salient Object Detection, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 12321\u201312328.","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"10.1016\/j.displa.2026.103533_b64","doi-asserted-by":"crossref","DOI":"10.1145\/3767748","article-title":"VM-UNet: Vision Mamba UNet for medical image segmentation","author":"Ruan","year":"2025","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.displa.2026.103533_b65","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVT v2: Improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.displa.2026.103533_b66","series-title":"U-Mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001964?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001964?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T11:46:10Z","timestamp":1779968770000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226001964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":66,"alternative-id":["S0141938226001964"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103533","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PolypLGLF: Layer-group feature extraction with low-coupling fusion network for polyp segmentation","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103533","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103533"}}