  Automatic breast lesion detection and classification is an important task in
computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common
and frequently used screening tool. Recently, a number of deep learning-based
methods have been proposed for joint localization and classification of breast
lesions using BUS images. In these methods, features extracted by a shared
network trunk are appended by two independent network branches to achieve
classification and localization. Improper information sharing might cause
conflicts in feature optimization in the two branches and leads to performance
degradation. Also, these methods generally require large amounts of pixel-level
annotated data for model training. To overcome these limitations, we proposed a
novel joint localization and classification model based on the attention
mechanism and disentangled semi-supervised learning strategy. The model used in
this study is composed of a classification network and an auxiliary
lesion-aware network. By use of the attention mechanism, the auxiliary
lesion-aware network can optimize multi-scale intermediate feature maps and
extract rich semantic information to improve classification and localization
performance. The disentangled semi-supervised learning strategy only requires
incomplete training datasets for model training. The proposed modularized
framework allows flexible network replacement to be generalized for various
applications. Experimental results on two different breast ultrasound image
datasets demonstrate the effectiveness of the proposed method. The impacts of
various network factors on model performance are also investigated to gain deep
insights into the designed framework.
