{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T10:31:45Z","timestamp":1756636305751,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.<\/jats:p>","DOI":"10.3233\/shti240603","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:00:04Z","timestamp":1724407204000},"source":"Crossref","is-referenced-by-count":1,"title":["Integrated and Interoperable Platform for Detecting Masses on Mammograms"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5848-7999","authenticated-orcid":false,"given":"Edouard","family":"Chatzopoulos","sequence":"first","affiliation":[{"name":"ICTEAM, UCLouvain, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6685-7398","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Jodogne","sequence":"additional","affiliation":[{"name":"ICTEAM, UCLouvain, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240603","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:00:05Z","timestamp":1724407205000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240603","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}