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Imaging"},{"key":"10.1016\/j.bspc.2026.110554_b72","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"554","article-title":"Mvc-net: Multi-view chest radiograph classification network with deep fusion","author":"Zhu","year":"2021"},{"key":"10.1016\/j.bspc.2026.110554_b73","article-title":"COVID-19 detection in X-ray images using convolutional neural networks","volume":"6","author":"Arias-Garz\u00f3n","year":"2021","journal-title":"Mach. Learn. Appl."},{"issue":"10","key":"10.1016\/j.bspc.2026.110554_b74","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","article-title":"Top-down neural attention by excitation backprop","volume":"126","author":"Zhang","year":"2018","journal-title":"Int. J. Comput. 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