Notice

Hit 164
Subject Realistic Mass Data Generation using Deep learning (by Hakmin Lee) is accepted in MICCAI 2019
Date 2019-06-05
Title: Realistic Breast Mass Generation through BIRADS Category
Authors: Hakmin Lee, Sung Tae Kim, Jae-Hyeok Lee and Yong Man Ro

Abstract: 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 characteristics 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.