  Automated breast ultrasound (ABUS) is a new and promising imaging modality
for breast cancer detection and diagnosis, which could provide intuitive 3D
information and coronal plane information with great diagnostic value. However,
manually screening and diagnosing tumors from ABUS images is very
time-consuming and overlooks of abnormalities may happen. In this study, we
propose a novel two-stage 3D detection network for locating suspected lesion
areas and further classifying lesions as benign or malignant tumors.
Specifically, we propose a 3D detection network rather than frequently-used
segmentation network to locate lesions in ABUS images, thus our network can
make full use of the spatial context information in ABUS images. A novel
similarity loss is designed to effectively distinguish lesions from background.
Then a classification network is employed to identify the located lesions as
benign or malignant. An IoU-balanced classification loss is adopted to improve
the correlation between classification and localization task. The efficacy of
our network is verified from a collected dataset of 418 patients with 145
benign tumors and 273 malignant tumors. Experiments show our network attains a
sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under
the curve(AUC) value of 0.8720.
