  Deep learning-based computer-aided diagnosis has achieved unprecedented
performance in breast cancer detection. However, most approaches are
computationally intensive, which impedes their broader dissemination in
real-world applications. In this work, we propose an efficient and
light-weighted multitask learning architecture to classify and segment breast
tumors simultaneously. We incorporate a segmentation task into a tumor
classification network, which makes the backbone network learn representations
focused on tumor regions. Moreover, we propose a new numerically stable loss
function that easily controls the balance between the sensitivity and
specificity of cancer detection. The proposed approach is evaluated using a
breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and
specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
We validate the model using a virtual mobile device, and the average inference
time is 0.35 seconds per image.
