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Previous deep learning based Computer Aided Diagnosis (CAD) system treatsmultiple views of the same lesion as independent images. Since an ultrasoundimage only describes a partial 2D projection of a 3D lesion, such paradigmignores the semantic relationship between different views of a lesion, which isinconsistent with the traditional diagnosis where sonographers analyze a lesionfrom at least two views. In this paper, we propose a multi-task framework thatcomplements Benign/Malignant classification task with lesion recognition (LR)which helps leveraging relationship among multiple views of a single lesion tolearn a complete representation of the lesion. To be specific, LR task employscontrastive learning to encourage representation that pulls multiple views ofthe same lesion and repels those of different lesions. The task thereforefacilitates a representation that is not only invariant to the view change ofthe lesion, but also capturing fine-grained features to distinguish betweendifferent lesions. Experiments show that the proposed multi-task frameworkboosts the performance of Benign/Malignant classification as two sub-taskscomplement each other and enhance the learned representation of ultrasoundimages.

Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis