An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

•We propose a novel neural network model for screening mammography interpretation•Our model outperforms popular models such as ResNet-34 and Faster R-CNN•Our model localizes malignant lesions in a weakly supervised manner•Our model requires less memory and is faster to train than ResNet-34 and Faster RCNN•Our model surpasses radiologist-level AUC by a margin of 0.11