BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations

Yuanhong Chen, Yuyuan Liu, Chong Wang, Michael Elliott, Chun Fung Kwok, Carlos A. Peña-Solórzano, Yu Tian, Fengbei Liu, Helen Frazer, Davis J. McCarthy, Gustavo Carneiro

Abstract: Highlights•We explore a new experimental setting and method for the detection of malignant breast lesions using large-scale real-world screening mammogram datasets that have both weak and fully annotations.•We proposed a new two-stage training method to jointly process GRAD-CAM and pseudo-label predictions in a student–teacher and cross-view manner.•We also propose innovations to the student–teacher framework to avoid the misalignment between the student and teacher’s parameter space.•We provide extensive experiments on two real-world breast cancer screening mammogram datasets containing incomplete annotations.