{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:45:39Z","timestamp":1769827539534,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Myocardial infarction (MI) is a prevalent and serious cardiovascular condition. The 12-lead electrocardiogram (ECG) is essential for MI diagnosis, as it reveals unique electrical patterns from different heart locations. Accurate localization and assessment of MI require a comprehensive analysis of ECG signals from multiple views. However, previous researches have primarily analyzed the 12-lead ECG from a single view, neglecting the variations in MI localization across different leads. Therefore, this study proposes a Multi-View Teacher-Student Framework (MV-TSF) for MI localization, which integrates multi-view learning and knowledge distillation. MV-TSF consists of two sub-networks: Multi-View Teacher network (MVT-net) and Single-View Student network (SVS-net). MVT-net treats the 12-lead ECG as five distinct views based on the correspondence between different heart regions and leads, and SVS-net uses the 12-lead ECG as input. Both sub-networks employ a multi-layer convolutional neural network structure for varied-scale ECG feature extraction. Additionally, MVT-net introduces an effective method for merging feature vectors from different views. Through knowledge distillation, knowledge is transferred from MVT-net to SVS-net, resulting in a Distilled SVS-net (DSVS-net) with only 0.35M parameters. Experimental results on two multi-label datasets indicate that DSVS-net is highly competitive, demonstrating exceptional parameter efficiency, inference speed, and model performance.<\/jats:p>","DOI":"10.3233\/faia251216","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:59Z","timestamp":1761126899000},"source":"Crossref","is-referenced-by-count":1,"title":["MV-TSF: A Novel Multi-View Teacher-Student Framework for Myocardial Infarction Localization"],"prefix":"10.3233","author":[{"given":"Yupeng","family":"Qiang","sequence":"first","affiliation":[{"name":"South China University of Technology, Guangzhou, 510641, China"}]},{"given":"Xunde","family":"Dong","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, 510641, China"}]},{"given":"Xiuling","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, 071002, China"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, 510641, China"}]},{"given":"Fei","family":"Hu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, 510641, China"}]},{"given":"Rongjia","family":"Wang","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, 510641, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251216","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:55:00Z","timestamp":1761126900000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251216","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}