AlexNet を用いたマンモグラフィ画像における乳腺濃度の自動分類

抄録

Breast cancer is the topmost incident cancer in Japanese women. Mammography is used for population-based screening of breast cancer, and mammary gland density is used for risk management. Four categories are defined for mammary gland density, and doctors and technicians perform qualitative visual classification. Therefore, objective estimation of mammary gland density is required. In this study, we propose an automatic classification method of mammary gland density in mammograms using a deep convolutional neural network（DCNN）. AlexNet is used for the DCNN, and five input image sets are prepared. The configuration is the original image only, the edge image only, and a combination of the original and edge images. In the edge image, the kernel size was set to 3 or 5. Finally, the mammary gland density was output from the four categories as the predicted classification result. Using the population-based screening data, 1106 mediolateral oblique images of right and left breasts were used. As a result, the average concordance rate between the predictive classification result and doctors' evaluation achieved 82.3% when only the original images was used.