Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks

Breast ultrasound (BUS) is an effective imaging modality for breast cancer diagnosis. The structural characteristics of breast lesions play an important role in computer-aided diagnosis. In this paper, a novel structure-aware triplet path network (SATPN) was designed to integrate classification and image reconstruction tasks to achieve accurate diagnosis on BUS images. Specifically, we enhanced clinically-approved structure characteristics of breast lesion by converting original BUS images to BI-RADS-oriented feature maps (BFMs) with a distance-transformation coupled Gaussian filter. Then, the converted BFMs were used as the inputs of the SATPN, which performed a supervised lesion classification task and two separate unsupervised stacked convolutional auto-encoder tasks for benign and malignant image reconstruction. We trained the SATPN with an alternative learning strategy by balancing image reconstruction error and classification label prediction error. The lesion label was determined by weighted voting of reconstruction error and label prediction error. We compared the performance of the SATPN with five deep learning methods using the original images and BFMs as inputs. Experimental results on two BUS datasets showed that SATPN performed the best among the six networks, with classification accuracy around 96%. These findings indicate that SATPN is promising for effective ultrasound computer-aided diagnosis of breast lesions.