Author, as appears in the article.: Saffari N; Rashwan HA; Herrera B; Romani S; Arenas M; Puig D
Department: Ciències Mèdiques Bàsiques Enginyeria Informàtica i Matemàtiques
URV's Author/s: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Arenas Prat, Meritxell / Herrera Gómez, Blas / Puig Valls, Domènec Savi / Romaní Also, Santiago / Saffari Tabalvandani, Nasibeh
Keywords: Mammograms Generative adversarial networks Deep learning Breast density estimation Breast cancer
Abstract: © 2018 The authors and IOS Press. 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 512×512 per second on a recent GPU.
Thematic Areas: Medicina ii Interdisciplinar Información y documentación General o multidisciplinar Engenharias iv Engenharias iii Comunicació i informació Ciências agrárias i Artificial intelligence
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 15356698
Author's mail: hatem.abdellatif@urv.cat nasibeh.saffarit@estudiants.urv.cat meritxell.arenas@urv.cat santiago.romani@urv.cat domenec.puig@urv.cat blas.herrera@urv.cat
Author identifier: 0000-0001-5421-1637 0000-0003-0815-2570 0000-0001-6673-9615 0000-0002-0562-4205 0000-0003-2924-9195
Record's date: 2024-09-21
Papper version: info:eu-repo/semantics/submittedVersion
Link to the original source: https://ebooks.iospress.nl/publication/50434
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Frontiers In Artificial Intelligence And Applications. 308 386-393
APA: Saffari N; Rashwan HA; Herrera B; Romani S; Arenas M; Puig D (2018). On Improving Breast Density Segmentation Using Conditional Generative Adversarial Networks. Amsterdam: IOS Press
Article's DOI: 10.3233/978-1-61499-918-8-386
Entity: Universitat Rovira i Virgili
Journal publication year: 2018
Publication Type: Proceedings Paper