  Breast ultrasound (US) is an effective imaging modality for breast cancer
detec-tion and diagnosis. The structural characteristics of breast lesion play
an im-portant role in Computer-Aided Diagnosis (CAD). In this paper, a novel
struc-ture-aware triplet path networks (SATPN) was designed to integrate
classifica-tion and two image reconstruction tasks to achieve accurate
diagnosis on US im-ages with small training dataset. Specifically, we enhance
clinically-approved breast lesion structure characteristics though converting
original breast US imag-es to BIRADS-oriented feature maps (BFMs) with a
distance-transformation coupled Gaussian filter. Then, the converted BFMs were
used as the inputs of SATPN, which performed lesion classification task and two
unsupervised stacked convolutional Auto-Encoder (SCAE) networks for benign and
malignant image reconstruction tasks, independently. We trained the SATPN with
an alter-native learning strategy by balancing image reconstruction error and
classification label prediction error. At the test stage, the lesion label was
determined by the weighted voting with reconstruction error and label
prediction error. We com-pared the performance of the SATPN with TPN using
original image as input and our previous developed semi-supervised deep
learning methods using BFMs as inputs. Experimental results on two breast US
datasets showed that SATPN ranked the best among the three networks, with
classification accuracy around 93.5%. These findings indicated that SATPN is
promising for effective breast US lesion CAD using small datasets.
