Improving the CNNs Performance of Mammography Mass Classification via Binary Mask Knowledge Transfer

Mammography is the primary screening method for lesion visualisation and detecting early changes in breast tissue. Deep learning, particularly convolutional neural networks (CNNs), are designed as tools to assist radiologists in the detection and classification of breast abnormalities. The application of deep learning models to mammography mass classification presents several challenges such as biased models caused by the lack of annotated mammographic images. We first defined the attention map of a CNN containing valuable information, especially shape knowledge from binary masks. Then we used knowledge transfer in which a CNN model transfers the attention map from binary masks to regions of interest (ROIs) to improve the performance of the CNN. When evaluating the developed approach on the BCDR dataset, DenseNet121 and ResNet-34 both achieve improved accuracy compared with the no-knowledge transfer on ROIs classification. For DenseNet121, the proposed method retrained the model with one transfer loss in the top layer and achieved improved accuracy of 71% compared to 58% for the no-knowledge transfer on ROIs classification. In addition, the resulting confusion matrix was more balanced.