{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/conditional-generative-adversarial-and","title":"Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification","arxiv_id":"1805.10207","date":"2018-05-25","proceeding":null,"authors":["Vivek Kumar Singh","Santiago Romani","Hatem A. Rashwan","Farhan Akram","Nidhi Pandey","Md. Mostafa Kamal Sarker","Jordina Torrents Barrena","Saddam Abdulwahab","Adel Saleh","Miguel Arquez","Meritxell Arenas","Domenec Puig"],"abstract":"This paper proposes a novel approach based on conditional Generative\nAdversarial Networks (cGAN) for breast mass segmentation in mammography. We\nhypothesized that the cGAN structure is well-suited to accurately outline the\nmass area, especially when the training data is limited. The generative network\nlearns intrinsic features of tumors while the adversarial network enforces\nsegmentations to be similar to the ground truth. Experiments performed on\ndozens of malignant tumors extracted from the public DDSM dataset and from our\nin-house private dataset confirm our hypothesis with very high Dice coefficient\nand Jaccard index (>94% and >89%, respectively) outperforming the scores\nobtained by other state-of-the-art approaches. Furthermore, in order to detect\nportray significant morphological features of the segmented tumor, a specific\nConvolutional Neural Network (CNN) have also been designed for classifying the\nsegmented tumor areas into four types (irregular, lobular, oval and round),\nwhich provides an overall accuracy about 72% with the DDSM dataset.","url_abs":"http://arxiv.org/abs/1805.10207v2","url_pdf":"http://arxiv.org/pdf/1805.10207v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"conditional-generative-adversarial-and","repo_url":"https://github.com/Violet981/Breast_mass_Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"conditional-generative-adversarial-and","repo_url":"https://github.com/ankit-ai/GAN_breast_mammography_segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}