MAMMOGRAPHIC MASS DETECTION BY BILATERAL ANALYSIS BASED ON CONVOLUTION NEURAL NETWORK

In this paper, a bilateral mass detection method is proposed for mammogram combining self-supervised learning network and Siamese-Faster-RCNN. The breast region is first identified by threshold segmentation and morphological filter. Then self-supervised learning network is built to learn the spatial transformation between the bilateral breast regions. Following bilateral mammograms are registered, a Siamese-Faster-RCNN consisting of the Region Proposal Network (RPN) and a Siamese fully connected (Siamese-FC) network is designed and employed for mass detection. The proposed method is estimated on two datasets (publicly available dataset INbreast and private dataset BCPKUPH). Experimental results show that the proposed method performs better than the previous state of art methods, which demonstrates the promise of the proposed method.