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.
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Acknowledgements
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence).
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Lee, H., Kim, S.T., Lee, JH., Ro, Y.M. (2019). Realistic Breast Mass Generation Through BIRADS Category. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_78
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DOI: https://doi.org/10.1007/978-3-030-32226-7_78
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