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However, training convolutional neural networks (CNNs) inevitably involves magnetic resonance (MR) images from multiple cohorts. There always exists variation in scanning protocol among cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Herein, a coarse mask\u2010guided deep domain adaptation network (CMD<jats:sup>2<\/jats:sup>A\u2010Net) is proposed to develop a fully automated framework for prostate lesion detection and classification (PLDC). No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion\u2010related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. Experiments are performed on 512 mpMRI sets from datasets of PROSTATEx (330 sets) and two cohorts, A (74 sets) and B (108 sets). Using ensemble learning, CMD<jats:sup>2<\/jats:sup>A\u2010Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large\u2010scale public dataset PROSTATEx to small\u2010scale target domains. Results from an ablation study also support its effectiveness in classification between benign and malignant lesions, compared to the state\u2010of\u2010the\u2010art models. An interactive preprint version of the article can be found here: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.22541\/au.166081031.11420810\/v1\">https:\/\/doi.org\/10.22541\/au.166081031.11420810\/v1<\/jats:ext-link>.<\/jats:p>","DOI":"10.1002\/aisy.202200246","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T21:25:11Z","timestamp":1688073911000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Multiparametric Magnetic Resonance Imaging\u2010Based Prostate Lesions Assessment with Unsupervised Domain Adaptation"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2169-3374","authenticated-orcid":false,"given":"Jing","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering The University of Hong Kong  Hong Kong 999077 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4569-6948","authenticated-orcid":false,"given":"Xiaomei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering The University of Hong Kong  Hong Kong 999077 China"},{"name":"Multi-Scale Medical Robotics Center Ltd.  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