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Deep learning-based computer-aided diagnosis has achieved unprecedentedperformance in breast cancer detection. However, most approaches arecomputationally intensive, which impedes their broader dissemination inreal-world applications. In this work, we propose an efficient andlight-weighted multitask learning architecture to classify and segment breasttumors simultaneously. We incorporate a segmentation task into a tumorclassification network, which makes the backbone network learn representationsfocused on tumor regions. Moreover, we propose a new numerically stable lossfunction that easily controls the balance between the sensitivity andspecificity of cancer detection. The proposed approach is evaluated using abreast ultrasound dataset with 1,511 images. The accuracy, sensitivity, andspecificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.We validate the model using a virtual mobile device, and the average inferencetime is 0.35 seconds per image.

EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer