深層畳み込みニューラルネットワークを用いた乳房X 線画像の乳腺濃度自動分類に関する予備的検討

抄録

Breast density is an important diagnostic information because it is related to detection sensitivity of breast cancer and cancer risk. However, since the observer classifies density subjectively, variations in judgment result from individual differences or experience differences. In this study, we developed a novel method for automated classification of mammograms using deep convolutional neural network（DCNN）. In the method, ninety-three mammograms from cancer screening programs were included in this study. A two-dimensional image was provided to the input layer of the DCNN. Four output units corresponding to four levels of breast density were obtained via two convolution, pooling, and fully connected layers. Here, as an input image, high-pass filtered images were given to the input layer in order to emphasize the skin line and the mammary glands in the mammogram. Furthermore, trimming of background of mammogram was conducted for the data augmentation. The evaluation of the 93 mammograms gave a correct classification rate of 86%. Moreover, when preprocessing（high-pass filter and trimming）were applied to the input image, the classification ability was improved as compared with the case where the mammogram was directly input. These results indicate that DCNN will be useful for breast density evaluation and risk assessment using mammograms.