{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:41:04Z","timestamp":1777336864500,"version":"3.51.4"},"reference-count":35,"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":["Neurocomputing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.neucom.2026.133582","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T01:13:34Z","timestamp":1775438014000},"page":"133582","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HETLesion: A hierarchical extended transformer framework for liver lesion segmentation in CT images with XAI"],"prefix":"10.1016","volume":"684","author":[{"given":"G.V.","family":"Pradeep Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Sireesha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.V.V.","family":"Satyanarayana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Naresh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0670-5138","authenticated-orcid":false,"given":"Alagar","family":"Karthick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinayagam","family":"Mohanavel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E.","family":"Parimalasundar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133582_bib1","article-title":"Automatic liver and tumour segmentation from CT images using Deep learning algorithm","volume":"6","author":"Manjunath","year":"2022","journal-title":"Results Control Optim."},{"key":"10.1016\/j.neucom.2026.133582_bib2","unstructured":"M. Heker, H. Greenspan, Joint liver lesion segmentation and classification via transfer learning (2020). arXiv preprint arXiv:2004.12352."},{"key":"10.1016\/j.neucom.2026.133582_bib3","series-title":"European Conference on Computer Vision","first-page":"448","article-title":"Co-heterogeneous and adaptive segmentation from multi-source and multi-phase CT imaging data: A study on pathological liver and lesion segmentation","author":"Raju","year":"2020"},{"issue":"1","key":"10.1016\/j.neucom.2026.133582_bib4","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1002\/mp.14585","article-title":"Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images","volume":"48","author":"Liu","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.neucom.2026.133582_bib5","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","first-page":"1173","article-title":"Hybrid cascaded neural network for liver lesion segmentation","author":"Dey","year":"2020"},{"issue":"10","key":"10.1016\/j.neucom.2026.133582_bib6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1002\/acm2.13003","article-title":"Deep learning and level set approach for liver and tumor segmentation from CT scans","volume":"21","author":"Alirr","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.neucom.2026.133582_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115406","article-title":"TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach","volume":"183","author":"Aghamohammadi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133582_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102331","article-title":"Deep learning for image-based liver analysis\u2014A comprehensive review focusing on malignant lesions","volume":"130","author":"Survarachakan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.neucom.2026.133582_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bea.2022.100043","article-title":"Modified U-NET on CT images for automatic segmentation of liver and its tumor","volume":"4","author":"Manjunath","year":"2022","journal-title":"Biomed. Eng. Adv."},{"key":"10.1016\/j.neucom.2026.133582_bib10","first-page":"1367","article-title":"A comparative analysis of deep learning methods for Alzheimer's disease diagnosis","author":"Muthukumar","year":"2025","journal-title":"2025 6th Int. Conf. IoT Based Control Netw. Intell. Syst. (ICICNIS) Bengal India"},{"key":"10.1016\/j.neucom.2026.133582_bib11","doi-asserted-by":"crossref","unstructured":"M. Rakshana, S. Umamaheswari and P. Muthukumar, \"The Impact of Artificial Intelligence on Engineering Applications,\" 2025 8th International Conference on Circuit, Power & Computing Technologies (ICCPCT), Kollam, India, 2025, pp. 2040-2044, doi: 10.1109\/ICCPCT65132.2025.11176549.L. Li, H. Ma, Rdctrans u-net: A hybrid variable architecture for liver ct image segmentation. Sensors 22(7) (2022) 2452.","DOI":"10.1109\/ICCPCT65132.2025.11176549"},{"key":"10.1016\/j.neucom.2026.133582_bib12","first-page":"1","article-title":"Deep learning-based automated breast cancer ultrasound image classification: a study","author":"Sivakumar","year":"2025","journal-title":"2025 Elev. Int. Conf. Bio Signals Images Instrum. (ICBSII) Chennai India"},{"key":"10.1016\/j.neucom.2026.133582_bib13","series-title":"Convolutional neural networks for medical image processing applications","first-page":"52","article-title":"Basic ensembles of vanilla-style deep learning models improve liver segmentation from ct images","author":"Kavur","year":"2022"},{"key":"10.1016\/j.neucom.2026.133582_bib14","series-title":"2020 IEEE international conference on image processing (ICIP)","first-page":"345","article-title":"Attention unet++: A nested attention-aware u-net for liver ct image segmentation","author":"Li","year":"2020"},{"key":"10.1016\/j.neucom.2026.133582_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102023","article-title":"Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention","volume":"113","author":"Chung","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.neucom.2026.133582_bib16","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference","first-page":"68","article-title":"Multi-phase liver tumor segmentation with spatial aggregation and uncertain region inpainting","author":"Zhang","year":"2021"},{"issue":"1","key":"10.1016\/j.neucom.2026.133582_bib17","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/9956983","article-title":"Automatic liver segmentation in CT images with enhanced GAN and mask region-based CNN architectures","volume":"2021","author":"Wei","year":"2021","journal-title":"BioMed. Res. Int."},{"issue":"7","key":"10.1016\/j.neucom.2026.133582_bib18","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1002\/mp.14922","article-title":"PA-ResSeg: a phase attention residual network for liver tumor segmentation from multi-phase CT images","volume":"48","author":"Xu","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.neucom.2026.133582_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102005","article-title":"Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images","volume":"70","author":"Zhang","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.133582_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101787","article-title":"Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks","volume":"65","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.133582_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107647","article-title":"Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers","volume":"240","author":"Hille","year":"2023","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"2","key":"10.1016\/j.neucom.2026.133582_bib22","doi-asserted-by":"crossref","first-page":"215","DOI":"10.3390\/bioengineering10020215","article-title":"Fully automatic liver and tumor segmentation from CT image using an AIM-Unet","volume":"10","author":"\u00d6zcan","year":"2023","journal-title":"Bioengineering"},{"issue":"17","key":"10.1016\/j.neucom.2026.133582_bib23","doi-asserted-by":"crossref","first-page":"7561","DOI":"10.3390\/s23177561","article-title":"Automatic liver tumor segmentation from CT images using graph convolutional network","volume":"23","author":"Khoshkhabar","year":"2023","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.neucom.2026.133582_bib24","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.32604\/csse.2023.030697","article-title":"Liver tumors segmentation using 3D SegNet deep learning approach","volume":"45","author":"Nallasivan","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"issue":"4","key":"10.1016\/j.neucom.2026.133582_bib25","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13927","article-title":"A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation","volume":"24","author":"Liu","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.neucom.2026.133582_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106501","article-title":"Eres-UNet++: liver CT image segmentation based on high-efficiency channel attention and Res-UNet++","volume":"158","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"10.1016\/j.neucom.2026.133582_bib27","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s00432-023-05564-7","article-title":"Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images","volume":"150","author":"Huang","year":"2024","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"10.1016\/j.neucom.2026.133582_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105561","article-title":"Reciprocal cross-modal guidance for liver lesion segmentation from multiple phases under incomplete overlap","volume":"88","author":"Yu","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.neucom.2026.133582_bib29","doi-asserted-by":"crossref","first-page":"9887","DOI":"10.1038\/s41598-024-60594-6","article-title":"Segmentation of liver CT images based on weighted medical transformer model","volume":"14","author":"Gu","year":"2024","journal-title":"Sci. Rep."},{"issue":"6","key":"10.1016\/j.neucom.2026.133582_bib30","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.3390\/sym15061181","article-title":"Image denoising method relying on iterative adaptive weight-mean filtering","volume":"15","author":"Wang","year":"2023","journal-title":"Symmetry"},{"key":"10.1016\/j.neucom.2026.133582_bib31","first-page":"1","article-title":"An improved image enhancement algorithm: radial contrast-limited adaptive histogram equalization","author":"Hu","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.neucom.2026.133582_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109259","article-title":"MAFA-TransUNet: Multi-scale attention and feature aggregation with transformer U-Net for liver tumor segmentation","volume":"113","author":"Li","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.133582_bib33","unstructured":"\u3008https:\/\/www.kaggle.com\/datasets\/kmader\/nih-deeplesion-subset\u3009."},{"key":"10.1016\/j.neucom.2026.133582_bib34","unstructured":"\u3008https:\/\/www.kaggle.com\/datasets\/andrewmvd\/liver-tumor-segmentation\/suggestions\u3009."},{"issue":"3","key":"10.1016\/j.neucom.2026.133582_bib35","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.5.3.036501","article-title":"DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning","volume":"5","author":"Yan","year":"2018","journal-title":"J. Med. Imaging"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226009793?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226009793?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T23:46:36Z","timestamp":1777333596000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226009793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":35,"alternative-id":["S0925231226009793"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133582","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HETLesion: A hierarchical extended transformer framework for liver lesion segmentation in CT images with XAI","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133582","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":"133582"}}