  Deep learning (DL) has proven highly effective for ultrasound-based
computer-aided diagnosis (CAD) of breast cancers. In an automaticCAD system,
lesion detection is critical for the following diagnosis. However, existing
DL-based methods generally require voluminous manually-annotated region of
interest (ROI) labels and class labels to train both the lesion detection and
diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may
not always be optimal for the classification task due to individual experience
of sonologists, resulting in the issue of coarse annotation that limits the
diagnosis performance of a CAD model. To address this issue, a novel Two-Stage
Detection and Diagnosis Network (TSDDNet) is proposed based on weakly
supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD
for breast cancers. In particular, all the ROI-level labels are considered as
coarse labels in the first training stage, and then a candidate selection
mechanism is designed to identify optimallesion areas for both the fully and
partially annotated samples. It refines the current ROI-level labels in the
fully annotated images and the detected ROIs in the partially annotated samples
with a weakly supervised manner under the guidance of class labels. In the
second training stage, a self-distillation strategy further is further proposed
to integrate the detection network and classification network into a unified
framework as the final CAD model for joint optimization, which then further
improves the diagnosis performance. The proposed TSDDNet is evaluated on a
B-mode ultrasound dataset, and the experimental results show that it achieves
the best performance on both lesion detection and diagnosis tasks, suggesting
promising application potential.
