Medical knowledge-guided deep learning for mammographic breast density classification

Long Gao; Chang Liu; Dooman Arefan; Ashok Panigrahy; Margarita L. Zuley; Shandong Wu

doi:10.1117/12.2655158

7 April 2023 Medical knowledge-guided deep learning for mammographic breast density classification

Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, Shandong Wu

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246536 (2023) https://doi.org/10.1117/12.2655158
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Classification of Breast Imaging Reporting and Data System (BI-RADS) breast density categories generally reflects the amount of dense/fibroglandular tissue in the breast. Studies have consistently shown that breast with higher density has a higher risk of developing breast cancer compared to breast with lower density. In this paper, we propose a novel end-to-end method, namely, Medical Knowledge-guided Deep Learning (MKDL), for breast mammogram density classification. The principle behind MKDL lies in the fact that many breast image density classification tasks are partly or largely based on certain pre-known image features, such as image contrast and brightness. These pre-known features can be computationally represented and then leveraged as prior knowledge to facilitate more effective model learning and thus boost the classification performance. We designed specific knowledge-based transformations for breast density classification and showed that our model outperformed several state-of-the-art models.

Citation Download Citation

Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, and Shandong Wu "Medical knowledge-guided deep learning for mammographic breast density classification", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246536 (7 April 2023); https://doi.org/10.1117/12.2655158

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