Weakly supervised Branch Network with Template Mask for Classifying Masses in 3D Automated Breast Ultrasound

Abstract

Automated breast ultrasound (ABUS) is being rapidly utilized for screening and diagnosing breast cancer. Breast masses, including cancers shown in ABUS scans, often appear as irregular hypoechoic areas that are hard to distinguish from background shadings. We propose a novel branch network architecture incorporating segmentation information of masses in the training process. By providing the spatial attention effect, the branch network boosts the performance of existing neural network classifiers, helping to learn meaningful features around the mass. For the segmentation information, we leverage the existing radiology reports without additional labeling efforts. The reports should include the characteristics of breast masses, such as shape and orientation, and a template mask can be created in a rule-based manner. Experimental results show that the proposed branch network with a template mask significantly improves the performance of existing classifiers.