A 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 BreastScreen Norway, the approach uses two convolutional neural networks. The first one is trained to classify whole images, and an explainable-AI method is applied to this network to identify a region of interest (ROI). The second neural network subsequently classifies the ROI for malignancy. While a prior method used simple gradient saliency maps to identify ROIs, a key enhancement of the present methodology is the application of Layered GradCam, which identifies cancerous areas more consistently and allows smaller ROIs. 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 scores indicate 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.