{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T21:04:41Z","timestamp":1784149481262,"version":"3.55.0"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82227802"],"award-info":[{"award-number":["82227802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2022CXGC010501"],"award-info":[{"award-number":["2022CXGC010501"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016109","name":"Taishan Industry Leading Talents","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016109","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF0715102"],"award-info":[{"award-number":["2023YFF0715102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.114880","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T07:06:06Z","timestamp":1776668766000},"page":"114880","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["A self-supervised visual auto-regressive framework for medical hyperspectral anomaly detection"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7075-6491","authenticated-orcid":false,"given":"Lingqin","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzhuang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhong","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4407-7045","authenticated-orcid":false,"given":"Xiaopeng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114880_b1","series-title":"Gpt-4 technical report","author":"Achiam","year":"2023"},{"issue":"12","key":"10.1016\/j.engappai.2026.114880_b2","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.19.12.126005","article-title":"Raman mapping of oral buccal mucosa: a spectral histopathology approach","volume":"19","author":"Behl","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"10.1016\/j.engappai.2026.114880_b3","series-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"issue":"4","key":"10.1016\/j.engappai.2026.114880_b4","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TII.2024.3523574","article-title":"VarAD: Lightweight high-resolution image anomaly detection via visual autoregressive modeling","volume":"21","author":"Cao","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.114880_b5","article-title":"Effective anomaly space for hyperspectral anomaly detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b6","doi-asserted-by":"crossref","first-page":"87430Q","DOI":"10.1117\/12.2015652","article-title":"Low rank decomposition-based anomaly detection for hyperspectral imagery","volume":"8743","author":"Chen","year":"2013","journal-title":"Proc. SPIE"},{"key":"10.1016\/j.engappai.2026.114880_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3470008","article-title":"Deep feature aggregation network for hyperspectral anomaly detection","volume":"73","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114880_b8","article-title":"Hyperspectral imaging for rapid impurity detection in power system liquids","volume":"158","author":"Cui","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b9","doi-asserted-by":"crossref","first-page":"39098","DOI":"10.1109\/ACCESS.2019.2904788","article-title":"In-vivo hyperspectral human brain image database for brain cancer detection","volume":"7","author":"Fabelo","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b10","article-title":"Medical hyperspectral imaging: a review","volume":"27","author":"Hu","year":"2022","journal-title":"J. Biomed. Opt."},{"issue":"5","key":"10.1016\/j.engappai.2026.114880_b11","first-page":"4139","article-title":"LREN: Low-rank embedded network for sample-free hyperspectral anomaly detection","volume":"35","author":"Jiang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b12","unstructured":"Kipf, T.N., Welling, M., 2017. Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations. pp. 1\u201314."},{"key":"10.1016\/j.engappai.2026.114880_b13","series-title":"A hyperspectral imaging dataset and methodology for intraoperative pixel-wise classification of metastatic colon cancer in the liver","author":"Kopriva","year":"2024"},{"issue":"2","key":"10.1016\/j.engappai.2026.114880_b14","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.30.2.023512","article-title":"Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications","volume":"30","author":"Kotwal","year":"2025","journal-title":"J. Biomed. Opt."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b15","article-title":"Hyperspectral image classification using deep learning models: a review","volume":"1950","author":"Kumar","year":"2021","journal-title":"J. Phys.: Conf. Ser."},{"issue":"2","key":"10.1016\/j.engappai.2026.114880_b16","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/TGRS.2004.841487","article-title":"Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery","volume":"43","author":"Kwon","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"10.1016\/j.engappai.2026.114880_b17","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"10.1016\/j.engappai.2026.114880_b18","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/TNNLS.2020.3038659","article-title":"Prior-based tensor approximation for anomaly detection in hyperspectral imagery","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"17","key":"10.1016\/j.engappai.2026.114880_b19","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.3390\/s24175664","article-title":"Hyperspectral anomaly detection based on spectral similarity variability feature","volume":"24","author":"Li","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114880_b20","first-page":"1","article-title":"You only train once: Learning a general anomaly enhancement network with random masks for hyperspectral anomaly detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b21","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/TGRS.2019.2936308","article-title":"Hyperspectral anomaly detection with kernel isolation forest","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b22","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1109\/TNNLS.2024.3355166","article-title":"GT-HAD: Gated transformer for hyperspectral anomaly detection","volume":"36","author":"Lian","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114880_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111151","article-title":"Progressive self-supervised framework for anomaly detection in hyperspectral images","volume":"156","author":"Liu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"10","key":"10.1016\/j.engappai.2026.114880_b24","doi-asserted-by":"crossref","first-page":"5557","DOI":"10.1109\/TNNLS.2021.3071026","article-title":"Multipixel anomaly detection with unknown patterns for hyperspectral imagery","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114880_b25","first-page":"1","article-title":"UADNet: A joint unmixing and anomaly detection network based on deep clustering for hyperspectral image","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b26","article-title":"MSNet: Self-supervised multiscale network with enhanced separation training for hyperspectral anomaly detection","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b27","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: a review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"10.1016\/j.engappai.2026.114880_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.107036","article-title":"Exploring hyperspectral anomaly detection with human vision: A small target aware detector","volume":"184","author":"Ma","year":"2025","journal-title":"Neural Netw."},{"issue":"4","key":"10.1016\/j.engappai.2026.114880_b29","article-title":"Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images","volume":"49","author":"Matteoli","year":"2010","journal-title":"Opt. Eng., Bellingham"},{"key":"10.1016\/j.engappai.2026.114880_b30","first-page":"1045","article-title":"Recurrent neural network based language model","author":"Mikolov","year":"2010","journal-title":"Proc. Interspeech"},{"issue":"2","key":"10.1016\/j.engappai.2026.114880_b31","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/JSTARS.2013.2238609","article-title":"Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data","volume":"6","author":"Molero","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114880_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107945","article-title":"Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment","volume":"132","author":"Olisah","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b33","article-title":"Transferable network with siamese architecture for anomaly detection in hyperspectral images","volume":"106","author":"Rao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"9","key":"10.1016\/j.engappai.2026.114880_b34","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/TMI.2017.2695523","article-title":"Manifold embedding and semantic segmentation for intraoperative guidance with hyperspectral brain imaging","volume":"36","author":"Ravi","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.engappai.2026.114880_b35","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"10.1016\/j.engappai.2026.114880_b36","first-page":"1824","article-title":"Joint subspace detection of hyperspectral targets","volume":"vol. 5","author":"Schaum","year":"2004"},{"issue":"12","key":"10.1016\/j.engappai.2026.114880_b37","first-page":"10295","article-title":"Hyperspectral image classification: A review","volume":"59","author":"Su","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b38","doi-asserted-by":"crossref","DOI":"10.1117\/1.JRS.8.083641","article-title":"Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery","volume":"8","author":"Sun","year":"2014","journal-title":"J. Appl. Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114880_b39","first-page":"194","article-title":"LSTM neural networks for language modeling","author":"Sundermeyer","year":"2012","journal-title":"Proc. Interspeech"},{"issue":"8","key":"10.1016\/j.engappai.2026.114880_b40","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1109\/TMI.2025.3563482","article-title":"Anomaly detection in medical images using encoder-attention-2decoders reconstruction","volume":"44","author":"Tang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.engappai.2026.114880_b41","series-title":"Visual autoregressive modeling: Scalable image generation via next-scale prediction","author":"Tian","year":"2024"},{"issue":"6","key":"10.1016\/j.engappai.2026.114880_b42","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.1109\/TNNLS.2022.3227167","article-title":"Hyperspectral anomaly detection using reconstruction fusion of quaternion frequency domain analysis","volume":"35","author":"Tu","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b43","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/TGRS.2020.2994205","article-title":"Hyperspectral image classification with context-aware dynamic graph convolutional network","volume":"59","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b44","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TCYB.2022.3175771","article-title":"Learning tensor low-rank representation for hyperspectral anomaly detection","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114880_b45","first-page":"1","article-title":"Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b46","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"9127","article-title":"Shift: A zero FLOP, zero parameter alternative to spatial convolutions","author":"Wu","year":"2018"},{"key":"10.1016\/j.engappai.2026.114880_b47","article-title":"Pixel-associated autoencoder for hyperspectral anomaly detection","volume":"129","author":"Xiang","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.engappai.2026.114880_b48","first-page":"1","article-title":"Confidence-weighted prior-guided RPCA for hyperspectral anomaly detection","author":"Xu","year":"2026","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.engappai.2026.114880_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107280","article-title":"Fuzzy graph convolutional network for hyperspectral image classification","volume":"127","author":"Xu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"10.1016\/j.engappai.2026.114880_b50","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1109\/TGRS.2015.2493201","article-title":"Anomaly detection in hyperspectral images based on low-rank and sparse representation","volume":"54","author":"Xu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b51","doi-asserted-by":"crossref","first-page":"6875","DOI":"10.1109\/JSTARS.2026.3660283","article-title":"TransGCF: A unified spatial\u2013spectral\u2013frequency framework for robust hyperspectral anomaly detection","volume":"19","author":"Xu","year":"2026","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111607","article-title":"Hyperspectral imaging for rapid impurity detection in power system liquids","volume":"158","author":"Xue","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b53","first-page":"1","article-title":"Spatial pooling graph convolutional network for hyperspectral image classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b54","article-title":"Medical hyperspectral image classification based weakly supervised single-image global learning network","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b55","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2024.3493879","article-title":"A real-time unsupervised hyperspectral band selection via spatial-spectral information fusion-based downscaled region","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b56","first-page":"2072","article-title":"Enhancing hyperspectral anomaly detection with a novel differential network approach for precision and robust background suppression","volume":"16","author":"Zhang","year":"2024","journal-title":"Remote. Sens."},{"key":"10.1016\/j.engappai.2026.114880_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110508","article-title":"Hyperspectral image mixed noised removal via jointly spatial and spectral difference constraint with low-rank tensor factorization","volume":"149","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114880_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111141","article-title":"A camouflage target classification method based on spectral difference enhancement and pixel-pair features in land-based hyperspectral images","volume":"156","author":"Zhao","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.114880_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104313","article-title":"Spectral-constrained global and local feature learning for hyperspectral anomaly detection","volume":"63","author":"Zhao","year":"2026","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.engappai.2026.114880_b60","article-title":"Hyperspectral image classification using quaternion convolutional neural networks","volume":"123","author":"Zhao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"10.1016\/j.engappai.2026.114880_b61","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1109\/TGRS.2016.2585495","article-title":"A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images","volume":"54","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2026.114880_b62","series-title":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"12994","article-title":"Improving medical large vision-language models with abnormal-aware feedback","author":"Zhou","year":"2025"},{"key":"10.1016\/j.engappai.2026.114880_b63","first-page":"25319","article-title":"MAM: Modular multi-agent framework for multi-modal medical diagnosis via role-specialized collaboration","author":"Zhou","year":"2025","journal-title":"Find. Assoc. Comput. Linguist.: ACL 2025"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626011620?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626011620?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:07:04Z","timestamp":1784146024000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626011620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":63,"alternative-id":["S0952197626011620"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114880","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A self-supervised visual auto-regressive framework for medical hyperspectral anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114880","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114880"}}