Two-stage mammography classification model using explainable-AI for ROI detection

Abstract

This study introduces an enhanced version of a two-stage modelling approach using artificial intelligence (AI) for breast cancer detection in mammography screening. Leveraging a large dataset of 2,863,175 mammograms from the Norwegian breast cancer screening program, the approach uses two convolutional neural networks. A key enhancement over the prior methodology is the application of the explainable-AI method Layered GradCam for identifying regions of interest (ROIs) within the mammograms. The second neural network subsequently classifies these ROIs for malignancy. Layered GradCam is also used to display identified cancers to the user. By the AUC criterion, our model performs well, 0.974 for screen-detected and 0.931 for all cancers (screen-detected and interval), compared to a commercial program; 0.959 and 0.918, respectively. Comparisons with the radiologist cores indicates that the model has equal performance with two radiologists, and superior performance to one, for the detection of all cancers (screening- and interval type). Our tests indicate that our model generalizes well for different breast centers, but so far only images from a single manufacturer have been tested.