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For instance, given a list of medical images and corresponding labels of patients' health status, it is often of greater importance to identify the image regions that could differentiate the outcome status, compared to simply predicting labels of new images. Moreover, medical image data often demonstrate strong individual heterogeneity. In other words, the image regions associated with an outcome could be different across patients. As a consequence, the traditional one\u2010model\u2010fits\u2010all approach not only omits patient heterogeneity but also possibly leads to misleading or even wrong conclusions. In this article, we introduce a novel statistical framework to detect individualized regions that are associated with a binary outcome, that is, whether a patient has a certain disease or not. Moreover, we propose a total variation\u2010based penalization for individualized image region detection under a local label\u2010free scenario. Considering that local labeling is often difficult to obtain for medical image data, our approach may potentially have a wider range of applications in medical research. The effectiveness of our proposed approach is validated by two real histopathology databases: Colon Cancer and Camelyon16.<\/jats:p>","DOI":"10.1002\/sam.11684","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T08:54:13Z","timestamp":1714553653000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Individualized image region detection with total variation"],"prefix":"10.1002","volume":"17","author":[{"given":"Sanyou","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,5]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2013.06.003"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"volume-title":"Machine learning approaches in medical image analysis: From detection to diagnosis","year":"2016","author":"De Bruijne M.","key":"e_1_2_7_5_1"},{"key":"e_1_2_7_6_1","unstructured":"R. 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