{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:53:25Z","timestamp":1762509205757,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685434"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"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":[[2024,9,25]]},"abstract":"<jats:p>Breast density is a crucial biomarker for predicting BC risk and recurrence. Women with dense breast tissues have a higher likelihood of developing BC, and dense tissue can obscure lesions, reducing detection sensitivity. Mammograms are vital for evaluating breast density, typically classified using the BI-RADS system. The main challenge in breast density segmentation is accurately localizing dense tissues. While segmentation models require detailed pixel-wise annotations, obtaining these labels is time-consuming and requires medical expertise. This paper proposes a weakly supervised approach for breast density localization, allowing deep neural network classifiers to generate saliency maps that highlight dense tissue regions based on image-level labels. We validate this model on the RSNA dataset and achieve a Dice score of 0.754, comparable to state-of-the-art supervised methods.<\/jats:p>","DOI":"10.3233\/faia240413","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:54Z","timestamp":1727689674000},"source":"Crossref","is-referenced-by-count":1,"title":["Weakly Supervised Localization of Mammograms for Dense Breast Tissues"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3490-1504","authenticated-orcid":false,"given":"Thamer","family":"Alsuhbani","sequence":"first","affiliation":[{"name":"DEIM, Rovira i Virgili University, 43007 Tarragona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9264-0301","authenticated-orcid":false,"given":"Ammar M.","family":"Okran","sequence":"additional","affiliation":[{"name":"DEIM, Rovira i Virgili University, 43007 Tarragona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8256-7753","authenticated-orcid":false,"given":"Shaima","family":"Algabli","sequence":"additional","affiliation":[{"name":"DEIM, Rovira i Virgili University, 43007 Tarragona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5421-1637","authenticated-orcid":false,"given":"Hatem A.","family":"Rashwan","sequence":"additional","affiliation":[{"name":"DEIM, Rovira i Virgili University, 43007 Tarragona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0562-4205","authenticated-orcid":false,"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[{"name":"DEIM, Rovira i Virgili University, 43007 Tarragona, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:54Z","timestamp":1727689674000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240413","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}