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Breast ultrasound (US) is an effective imaging modality for breast cancerdetec-tion and diagnosis. The structural characteristics of breast lesion playan im-portant role in Computer-Aided Diagnosis (CAD). In this paper, a novelstruc-ture-aware triplet path networks (SATPN) was designed to integrateclassifica-tion and two image reconstruction tasks to achieve accuratediagnosis on US im-ages with small training dataset. Specifically, we enhanceclinically-approved breast lesion structure characteristics though convertingoriginal breast US imag-es to BIRADS-oriented feature maps (BFMs) with adistance-transformation coupled Gaussian filter. Then, the converted BFMs wereused as the inputs of SATPN, which performed lesion classification task and twounsupervised stacked convolutional Auto-Encoder (SCAE) networks for benign andmalignant image reconstruction tasks, independently. We trained the SATPN withan alter-native learning strategy by balancing image reconstruction error andclassification label prediction error. At the test stage, the lesion label wasdetermined by the weighted voting with reconstruction error and labelprediction error. We com-pared the performance of the SATPN with TPN usingoriginal image as input and our previous developed semi-supervised deeplearning methods using BFMs as inputs. Experimental results on two breast USdatasets showed that SATPN ranked the best among the three networks, withclassification accuracy around 93.5%. These findings indicated that SATPN ispromising for effective breast US lesion CAD using small datasets.

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