{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T12:59:40Z","timestamp":1785243580775,"version":"3.55.0"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bspc.2026.110024","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:31:57Z","timestamp":1773250317000},"page":"110024","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["TransEdgeNet: a self-supervised hybrid network with token pruning and temporal-spatial attention for real-time medical image segmentation"],"prefix":"10.1016","volume":"120","author":[{"given":"Dhyanendra","family":"jain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamal","family":"Borana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uma","family":"Tomer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit Kumar","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.bspc.2026.110024_b0005","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"Pinto-Coelho","year":"2023","journal-title":"Bioengineering"},{"issue":"1","key":"10.1016\/j.bspc.2026.110024_b0010","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/5164970","article-title":"Modern diagnostic imaging technique applications and risk factors in the medical field: a review","volume":"2022","author":"Hussain","year":"2022","journal-title":"Biomed Res. Int."},{"key":"10.1016\/j.bspc.2026.110024_b0015","first-page":"117","article-title":"Precise segmentation techniques in various medical images","author":"Harouni","year":"2021","journal-title":"Artif. Intell. Internet Things"},{"key":"10.1016\/j.bspc.2026.110024_b0020","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.439","article-title":"Image thresholding approaches for medical image segmentation-short literature review","volume":"219","author":"Jardim","year":"2023","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110024_b0025","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/s25010087","article-title":"Comparative analysis of edge detection operators using a threshold estimation approach on medical noisy images with different complexities","volume":"25","author":"Maksimovic","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110024_b0030","article-title":"MedTransCluster: transfer learning for deep medical image clustering","volume":"9","author":"Jahanian","year":"2024","journal-title":"Intell.-Based Med."},{"issue":"12","key":"10.1016\/j.bspc.2026.110024_b0035","doi-asserted-by":"crossref","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","article-title":"A survey on self-supervised learning: Algorithms, applications, and future trends","volume":"46","author":"Gui","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.bspc.2026.110024_b0040","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.3390\/cancers16061120","article-title":"Improving skin lesion segmentation with self-training","volume":"16","author":"Dzieniszewska","year":"2024","journal-title":"Cancers"},{"key":"10.1016\/j.bspc.2026.110024_b0045","series-title":"Medical Imaging with Deep Learning","first-page":"666","article-title":"ViT-AE++: improving vision transformer autoencoder for self-supervised medical image representations","author":"Prabhakar","year":"2024"},{"key":"10.1016\/j.bspc.2026.110024_b0050","unstructured":"Z. Chen, B. Hu, Z. Chen, & J. Zhang, Progress and thinking on self-supervised learning methods in computer vision: A review. IEEE Sensors Journal."},{"key":"10.1016\/j.bspc.2026.110024_b0055","first-page":"1","article-title":"Attention-guided fusion of transformers and CNNs for enhanced medical image segmentation","volume":"2024","author":"Zhou","year":"2025","journal-title":"Vis. Comput."},{"key":"10.1016\/j.bspc.2026.110024_b0060","unstructured":"L. Upase, & N. R. Upadhya, The Evolution of Transformers Architecture in Natural Language Processing (2024)."},{"key":"10.1016\/j.bspc.2026.110024_b0065","article-title":"Deep learning and computer vision techniques for enhanced quality control in manufacturing processes","author":"Islam","year":"2024","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.bspc.2026.110024_b0070","article-title":"Deep learning approaches for medical image analysis and diagnosis","volume":"16","author":"Thakur","year":"2024","journal-title":"Cureus"},{"key":"10.1016\/j.bspc.2026.110024_b0075","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"297","article-title":"Contrans: Improving transformer with convolutional attention for medical image segmentation","author":"Lin","year":"2022"},{"key":"10.1016\/j.bspc.2026.110024_b0080","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"404","article-title":"Mist: Medical image segmentation transformer with convolutional attention mixing (cam) decoder","author":"Rahman","year":"2024"},{"key":"10.1016\/j.bspc.2026.110024_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2024.100346","article-title":"BT-Net: an end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images","volume":"22","author":"Rabby","year":"2024","journal-title":"Array"},{"key":"10.1016\/j.bspc.2026.110024_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110077","article-title":"An ensemble model with attention based mechanism for image captioning","volume":"123","author":"Al Badarneh","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.bspc.2026.110024_b0095","series-title":"International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)","first-page":"1343","article-title":"Comparative analysis of vision transformer and CNN architectures in medical image classification","author":"Sharma","year":"2025"},{"issue":"2","key":"10.1016\/j.bspc.2026.110024_b0100","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11548-023-03024-8","article-title":"Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention","volume":"19","author":"Ghazouani","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.bspc.2026.110024_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103547","article-title":"Medical sam adapter: Adapting segment anything model for medical image segmentation","volume":"102","author":"Wu","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110024_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103554","article-title":"MedScale-Former: Self-guided multi-scale transformer for medical image segmentation","volume":"103","author":"Karimijafarbigloo","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110024_b0115","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2024.3519634","article-title":"Hresformer: hybrid residual transformer for volumetric medical image segmentation","author":"Ren","year":"2025","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.bspc.2026.110024_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103545","article-title":"LW-CTrans: a lightweight hybrid network of CNN and Transformer for 3D medical image segmentation","volume":"102","author":"Kuang","year":"2025","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.bspc.2026.110024_b0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12559-025-10425-1","article-title":"Gsac-uformer: Groupwise self-attention convolutional transformer-based unet for medical image segmentation","volume":"17","author":"Garbaz","year":"2025","journal-title":"Cogn. Comput."},{"issue":"2","key":"10.1016\/j.bspc.2026.110024_b0130","doi-asserted-by":"crossref","first-page":"20","DOI":"10.24843\/JIK.2021.v14.i01.p03","article-title":"Analysis of medical image resizing using bicubic interpolation algorithm","volume":"14","author":"Triwijoyo","year":"2021","journal-title":"J. Ilmu Komput"},{"key":"10.1016\/j.bspc.2026.110024_b0135","article-title":"When to use standardization and normalization: empirical evidence from machine learning models and XAI","author":"Sujon","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110024_b0140","doi-asserted-by":"crossref","unstructured":"L. C. Chen, G. Papandreou, F. Schroff, & H. Adam, Rethinking atrous convolution for semantic image segmentation.arXiv preprint arXiv:1706.05587. (2017).","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.1016\/j.bspc.2026.110024_b0145","doi-asserted-by":"crossref","first-page":"164546","DOI":"10.1109\/ACCESS.2020.3021739","article-title":"Semantic segmentation of litchi branches using DeepLabV3+ model","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.bspc.2026.110024_b0150","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.33.6.063007","article-title":"L-DeeplabV3+: a lightweight semantic segmentation algorithm for complex scene perception","volume":"33","author":"Fei","year":"2024","journal-title":"J. Electron. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.110024_b0155","doi-asserted-by":"crossref","first-page":"441","DOI":"10.20895\/infotel.v16i2.1144","article-title":"Image segmentation performance using Deeplabv3+ with Resnet-50 on autism facial classification","volume":"16","author":"Melinda","year":"2024","journal-title":"JURNAL INFOTEL"},{"issue":"3","key":"10.1016\/j.bspc.2026.110024_b0160","doi-asserted-by":"crossref","first-page":"516","DOI":"10.3390\/rs13030516","article-title":"Vision transformers for remote sensing image classification","volume":"13","author":"Bazi","year":"2021","journal-title":"Remote Sens. (Basel)"},{"issue":"1","key":"10.1016\/j.bspc.2026.110024_b0165","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s10664-024-10558-1","article-title":"SparseCoder: advancing source code analysis with sparse attention and learned token pruning","volume":"30","author":"Yang","year":"2025","journal-title":"Empir. Softw. Eng."},{"key":"10.1016\/j.bspc.2026.110024_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.104030","article-title":"Other tokens matter: exploring global and local features of vision transformers for object re-identification","volume":"244","author":"Wang","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.bspc.2026.110024_b0175","doi-asserted-by":"crossref","unstructured":"C. Tai, R. Qiu, Z. He, X. Jie, & Y. Jianhong, The Role of Token Pruning in Efficient Transformer Architectures (2025).","DOI":"10.36227\/techrxiv.174114507.73438420\/v1"},{"key":"10.1016\/j.bspc.2026.110024_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106883","article-title":"Pigment network detection and classification in dermoscopic images using directional imaging algorithms and convolutional neural networks","volume":"99","author":"Rasel","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"10.1016\/j.bspc.2026.110024_b0185","article-title":"ADC-MambaNet: a lightweight U-shaped architecture with mamba and multi-dimensional priority attention for medical image segmentation","volume":"11","author":"Ho","year":"2025","journal-title":"Biomed. Phys. Eng. Express"},{"key":"10.1016\/j.bspc.2026.110024_b0190","unstructured":"M. Zhang, X. Dai, Y. Sun, J. Wang, Y. Yao, X. Gong,... & Y. Lv, Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework.arXiv preprint arXiv:2506.02854. (2025)."},{"key":"10.1016\/j.bspc.2026.110024_b0195","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"481","article-title":"Ege-unet: an efficient group enhanced unet for skin lesion segmentation","author":"Ruan","year":"2023"},{"issue":"7","key":"10.1016\/j.bspc.2026.110024_b0200","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.3390\/app14072741","article-title":"Automatic medical image segmentation with vision transformer","volume":"14","author":"Zhang","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.110024_b0205","first-page":"1","article-title":"D2U-net: a dual-path hybrid UNet architecture for precise medical image segmentation","author":"Ahmed","year":"2025","journal-title":"Vis. Comput."},{"key":"10.1016\/j.bspc.2026.110024_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110024_b0215","unstructured":"A. Dosovitskiy, An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. (2020)."},{"key":"10.1016\/j.bspc.2026.110024_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111274","article-title":"kMaXU: medical image segmentation U-Net with k-means Mask Transformer and contrastive cluster assignment","volume":"161","author":"Huang","year":"2025","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.bspc.2026.110024_b0225","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"issue":"1","key":"10.1016\/j.bspc.2026.110024_b0230","doi-asserted-by":"crossref","first-page":"9716","DOI":"10.1038\/s41598-024-60375-1","article-title":"An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110024_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107154","article-title":"PFormer: an efficient CNN-transformer hybrid network with content-driven P-attention for 3D medical image segmentation","volume":"101","author":"Gao","year":"2025","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426005781?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426005781?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T12:03:59Z","timestamp":1785240239000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426005781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":47,"alternative-id":["S1746809426005781"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110024","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TransEdgeNet: a self-supervised hybrid network with token pruning and temporal-spatial attention for real-time medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110024","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":"110024"}}