H. Lee, S.T. Kim, J.Lee, Y.M. Ro Realistic Breast Mass Generation through BIRADS Category 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)
	Generating realistic breast masses is a highly important task because the large-size database of annotated breast masses is scarcely available. In this study, a novel realistic breast mass generation framework using the characteris-tics of the breast mass (i.e. BIRADS category) has been devised. For that purpose, the visual-semantic BIRADS description for characterizing breast masses is embedded into the deep network. The visual-semantic description is encoded together with image features and used to generate the realistic masses according the visual-semantic description. To verify the effectiveness of the proposed method, two public mammogram datasets were used. Qualitative and quantitative experimental results have shown that the realistic breast masses could be generated according to the BIRADs category.
	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.