Applying graph convolution neural network in digital breast tomosynthesis for cancer classification

Digital breast tomosynthesis, or 3D mammography, has advanced the field of breast imaging diagnosis. It has been rapidly replacing the traditional full-field digital mammography because of its diagnostic superiority. However, automatic detection of breast cancer using digital breast tomosynthesis images has remained challenging, mainly due to their high resolution, high volume, and complexity. In this study, we developed a novel model for more precise detection of cancerous 3D mammogram images. The proposed model first, represents 3D mammograms as graphs, then employs a self-attention graph convolutional neural network model to effectively and efficiently learn the features of 3D mammograms, and finally, using the extracted features, identifies the cancerous 3D mammograms. We trained and evaluated the performance of the proposed model using public and private datasets. We compared the performance of the proposed model with those of multiple state-of-the-art CNN-based models as baseline models. The results show that the proposed model outperforms all the baseline models in terms of accuracy, precision, sensitivity, F1, and AUC.