{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:26:41Z","timestamp":1740202001002,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Breast density is a crucial factor to follow-up the relapse of breast cancer in mammograms and the risk of local recurrence after conservative surgery and\/or radiotherapy. Accurate breast density estimation with visual assessment is still a challenge due to faint contrast and significant variations in background fatty tissues in mammograms. The important key of breast density estimation is to properly detect the dense tissues in a mammographic image. Thus, this paper presents an automatic deep breast density segmentation using conditional Generative Adversarial Networks (cGAN) that consist of two successive deep networks: generator and discriminator. The generator network learns the mapping from the input mammogram to the output binary mask detection the area of the dense tissues. In turn, the discriminator learns a loss function to train this mapping by comparing the ground-truth and the predicted mask under observing the input mammogram as a condition. The performance of the proposed model was evaluated on the public INbreast mammographic datasets. The proposed model can segment the dense regions with overall recall, precision and F-score about 95%, 92%, and 93%, respectively, outperforming state-of-the-art of breast density segmentation. The proposed model can segment more than 40 images with a size of 512&amp;times;512 per second on a recent GPU.<\/jats:p>","DOI":"10.3233\/978-1-61499-918-8-386","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":0,"title":["On Improving Breast Density Segmentation Using Conditional Generative Adversarial Networks"],"prefix":"10.3233","author":[{"family":"Saffari Nasibeh","sequence":"additional","affiliation":[]},{"family":"Rashwan Hatem A.","sequence":"additional","affiliation":[]},{"family":"Herrera Blas","sequence":"additional","affiliation":[]},{"family":"Romani Santiago","sequence":"additional","affiliation":[]},{"family":"Arenas Meritxell","sequence":"additional","affiliation":[]},{"family":"Puig Dom&egrave;nec","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:52:07Z","timestamp":1740135127000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-917-1&spage=386&doi=10.3233\/978-1-61499-918-8-386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-918-8-386","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}