{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T00:01:33Z","timestamp":1784937693767,"version":"3.55.0"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015063","name":"National Institute of Technology Calicut","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015063","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133748","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T16:11:32Z","timestamp":1776701492000},"page":"133748","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TRACS: A generalizable triple attention network for self-supervised coronary vessel segmentation"],"prefix":"10.1016","volume":"693","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7007-6586","authenticated-orcid":false,"given":"Bhupender","family":"Kaushal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudhish N.","family":"George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Varma MK","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiran","family":"Raja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neucom.2026.133748_bib0005","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-12602-6","article-title":"A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation","volume":"15","author":"Banerjee","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2026.133748_bib0010","first-page":"1","article-title":"A comprehensive study of enhanced computational approaches for breast cancer classification: comparative analysis with existing state of the art methods","author":"Singh","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"issue":"1","key":"10.1016\/j.neucom.2026.133748_bib0015","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-11574-x","article-title":"Pyramidal attention-based t network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches","volume":"15","author":"Banerjee","year":"2025","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.neucom.2026.133748_bib0020","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202300706","article-title":"Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3D convolutions and long short-term memory modules","volume":"6","author":"Ul Amin","year":"2024","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.neucom.2026.133748_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2025.108500","article-title":"Towards automated and reliable lung cancer detection in histopathological images using dy-fspan: a feature-summarized pyramidal attention network for explainable AI","author":"Banerjee","year":"2025","journal-title":"Computational Biology and Chemistry"},{"key":"10.1016\/j.neucom.2026.133748_bib0030","doi-asserted-by":"crossref","first-page":"154080","DOI":"10.1109\/ACCESS.2024.3480205","article-title":"Detection of violent scenes in cartoon movies using a deep learning approach","volume":"12","author":"Khan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133748_bib0035","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and its variants for medical image segmentation: a review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.neucom.2026.133748_bib0040","doi-asserted-by":"crossref","first-page":"600","DOI":"10.55730\/1300-0152.2766","article-title":"A systematic review of machine learning in heart disease prediction","volume":"49","author":"Banerjee","year":"2025","journal-title":"Turkish Journal of Biology"},{"issue":"7855","key":"10.1016\/j.neucom.2026.133748_bib0045","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1038\/s41586-021-03392-8","article-title":"The changing landscape of atherosclerosis","volume":"592","author":"Libby","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2026.133748_bib0050","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.patcog.2018.09.015","article-title":"Accurate vessel extraction via tensor completion of background layer in x-ray coronary angiograms","volume":"87","author":"Qin","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.133748_bib0055","series-title":"IEEE International Conference on Computer Vision (ICCV)","article-title":"Self-supervised vessel segmentation via adversarial learning","author":"Ma","year":"2021"},{"issue":"1","key":"10.1016\/j.neucom.2026.133748_bib0060","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/8365783","article-title":"A hybrid unsupervised approach for retinal vessel segmentation","volume":"2020","author":"Khan","year":"2020","journal-title":"Biomed Res. Int."},{"key":"10.1016\/j.neucom.2026.133748_bib0065","author":"Ronneberger"},{"issue":"2","key":"10.1016\/j.neucom.2026.133748_bib0070","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TPAMI.1986.4767772","article-title":"Angy: a rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms","volume":"PAMI-8","author":"Stansfield","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133748_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109926","article-title":"Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence","volume":"145","author":"Fu","year":"2024","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.neucom.2026.133748_bib0080","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/42.996343","article-title":"An x-ray-based method for the determination of the contrast agent propagation in 3-d vessel structures","volume":"21","author":"Schmitt","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.neucom.2026.133748_bib0085","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/s12194-008-0031-4","article-title":"Automated segmentation of hepatic vessels in non-contrast x-ray ct images","volume":"1","author":"Kawajiri","year":"2008","journal-title":"Radiol. Phys. Technol."},{"issue":"6","key":"10.1016\/j.neucom.2026.133748_bib0090","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s10278-013-9585-8","article-title":"A new blood vessel extraction technique using edge enhancement and object classification","volume":"26","author":"Badsha","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.neucom.2026.133748_bib0095","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"400","article-title":"A domain-adaptive two-stream U-Net for electron microscopy image segmentation","author":"Berm\u00fadez-Chac\u00f3n","year":"2018"},{"key":"10.1016\/j.neucom.2026.133748_bib0100","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"1519","article-title":"Domain adaptive segmentation in volume electron microscopy imaging","author":"Roels","year":"2019"},{"key":"10.1016\/j.neucom.2026.133748_bib0105","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9865","article-title":"Invariant information clustering for unsupervised image classification and segmentation","author":"Ji","year":"2019"},{"key":"10.1016\/j.neucom.2026.133748_bib0110","article-title":"Unsupervised object segmentation by redrawing","volume":"32","author":"Chen","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133748_bib0115","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Diffusion adversarial representation learning for self-supervised vessel segmentation","author":"Kim","year":"2023"},{"key":"10.1016\/j.neucom.2026.133748_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2025.108368","article-title":"Cicada (ucx): a novel approach for automated breast cancer classification through aggressiveness delineation","volume":"115","author":"Singh","year":"2025","journal-title":"Computational Biology and Chemistry"},{"key":"10.1016\/j.neucom.2026.133748_bib0125","first-page":"1","article-title":"A comprehensive study on deep learning models for the detection of diabetic retinopathy using pathological images","author":"Singh","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.neucom.2026.133748_bib0130","first-page":"1","article-title":"A comparative evaluation of deep learning architectures for prostate cancer segmentation: introducing trionixnet with n-core multi-attention mechanism","author":"Narayan","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.neucom.2026.133748_bib0135","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.neucom.2026.133748_bib0140","author":"Dosovitskiy"},{"key":"10.1016\/j.neucom.2026.133748_bib0145","series-title":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1456","article-title":"Vision transformer-based retina vessel segmentation with deep adaptive gamma correction","author":"Yu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133748_bib0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111655","article-title":"Enhancing pine wilt disease detection with synthetic data and external attention-based transformers","volume":"159","author":"Amin","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133748_bib0155","first-page":"1","article-title":"Advances in deep neural, transformer learning, and kernel-based methods for diabetic retinopathy detection: a comprehensive review","author":"Banerjee","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.neucom.2026.133748_bib0160","author":"Feng"},{"key":"10.1016\/j.neucom.2026.133748_bib0165","series-title":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Unsupervised domain adaptation for cross-modality retinal vessel segmentation via disentangling representation style transfer and collaborative consistency learning","author":"Peng","year":"2022"},{"issue":"11","key":"10.1016\/j.neucom.2026.133748_bib0170","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.neucom.2026.133748_bib0175","series-title":"2021 IEEE International Conference on Image Processing (ICIP)","first-page":"26","article-title":"Feature disentanglement for cross-domain retina vessel segmentation","author":"Wang","year":"2021"},{"key":"10.1016\/j.neucom.2026.133748_bib0180","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"599","article-title":"Task driven generative modeling for unsupervised domain adaptation: application to x-ray image segmentation","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2026.133748_bib0185","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22","first-page":"111","article-title":"Unsupervised ensemble strategy for retinal vessel segmentation","author":"Liu","year":"2019"},{"key":"10.1016\/j.neucom.2026.133748_bib0190","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"14502","article-title":"Self-supervised learning of object parts for semantic segmentation","author":"Ziegler","year":"2022"},{"issue":"4","key":"10.1016\/j.neucom.2026.133748_bib0195","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11831-023-09884-2","article-title":"Self-supervised learning: a succinct review","volume":"30","author":"Rani","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.neucom.2026.133748_bib0200","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1422","article-title":"Unsupervised visual representation learning by context prediction","author":"Doersch","year":"2015"},{"key":"10.1016\/j.neucom.2026.133748_bib0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108460","article-title":"Fine-grained self-supervised learning with jigsaw puzzles for medical image classification","volume":"174","author":"Park","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.133748_bib0210","series-title":"Advances in Neural Information Processing Systems","first-page":"59337","article-title":"Hassod: hierarchical adaptive self-supervised object detection","volume":"vol. 36","author":"Cao","year":"2023"},{"key":"10.1016\/j.neucom.2026.133748_bib0215","article-title":"Conmamba: contrastive vision mamba for plant disease detection","author":"Al Mamun","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.133748_bib0220","author":"Ciocarlan"},{"key":"10.1016\/j.neucom.2026.133748_bib0225","author":"Mamun al"},{"key":"10.1016\/j.neucom.2026.133748_bib0230","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops","first-page":"187","article-title":"Deepvat: a self-supervised technique for cluster assessment in image datasets","author":"Mazumder","year":"2023"},{"key":"10.1016\/j.neucom.2026.133748_bib0235","author":"Wang"},{"key":"10.1016\/j.neucom.2026.133748_bib0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102879","article-title":"Dive into the details of self-supervised learning for medical image analysis","volume":"89","author":"Zhang","year":"2023","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.neucom.2026.133748_bib0245","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1007\/s12530-024-09581-w","article-title":"Self-supervised learning for medical image analysis: a comprehensive review","volume":"15","author":"Rani","year":"2024","journal-title":"Evolving Systems"},{"key":"10.1016\/j.neucom.2026.133748_bib0250","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.neunet.2020.05.005","article-title":"Sequential vessel segmentation via deep channel attention network","volume":"128","author":"Hao","year":"2020","journal-title":"Neural Netw."},{"issue":"24","key":"10.1016\/j.neucom.2026.133748_bib0255","doi-asserted-by":"crossref","first-page":"5507","DOI":"10.3390\/app9245507","article-title":"Automatic segmentation of coronary arteries in x-ray angiograms using multiscale analysis and artificial neural networks","volume":"9","author":"Cervantes-Sanchez","year":"2019","journal-title":"Appl. Sci."},{"issue":"4","key":"10.1016\/j.neucom.2026.133748_bib0260","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.neucom.2026.133748_bib0265","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","article-title":"Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133748_bib0270","author":"Hu"},{"key":"10.1016\/j.neucom.2026.133748_bib0275","author":"Misra"},{"key":"10.1016\/j.neucom.2026.133748_bib0280","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2337","article-title":"Semantic image synthesis with spatially-adaptive normalization","author":"Park","year":"2019"},{"key":"10.1016\/j.neucom.2026.133748_bib0285","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2794","article-title":"Least squares generative adversarial networks","author":"Mao","year":"2017"},{"key":"10.1016\/j.neucom.2026.133748_bib0290","author":"Park"},{"key":"10.1016\/j.neucom.2026.133748_bib0295","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Cldice - a novel topology-preserving loss function for tubular structure segmentation","author":"Shit","year":"2021"},{"key":"10.1016\/j.neucom.2026.133748_bib0300","series-title":"2018 IEEE Winter Conference on Applications of Computer Vision (WACV)","first-page":"839","article-title":"Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks","author":"Chattopadhay","year":"2018"},{"key":"10.1016\/j.neucom.2026.133748_bib0305","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.neucom.2026.133748_bib0310","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"Why should i trust you?\u201d Explaining the predictions of any classifier, in","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.neucom.2026.133748_bib0315","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133748_bib0320","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"25198","article-title":"Do imagenet-trained models learn shortcuts? The impact of frequency shortcuts on generalization","author":"Wang","year":"2025"},{"key":"10.1016\/j.neucom.2026.133748_bib0325","author":"Ahn"},{"key":"10.1016\/j.neucom.2026.133748_bib0330","author":"Melas-Kyriazi"},{"issue":"11","key":"10.1016\/j.neucom.2026.133748_bib0335","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1109\/TMI.2019.2927182","article-title":"Deep adversarial training for multi-organ nuclei segmentation in histopathology images","volume":"39","author":"Mahmood","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133748_bib0340","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2223","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011458?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011458?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:05:47Z","timestamp":1784934347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226011458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":68,"alternative-id":["S0925231226011458"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133748","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TRACS: A generalizable triple attention network for self-supervised coronary vessel segmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133748","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133748"}}