Integrating Interpretability into Deep Learning Models for Mammogram-Based Breast Cancer Detection

Breast cancer is one of the most common cancers found in women, affecting 1 in 8 Canadian women over their lifetime, according to the Public Health Agency of Canada. The early diagnosis of breast cancer can be extremely beneficial for reducing the spread of cancer cells and the risk of death. Machine learning (ML) systems can help healthcare professionals diagnose breast cancer with better accuracy by identifying underlying patterns in the images that radiologists might find difficult to detect early on. While deep learning (DL) models can obtain high accuracies, their black-box nature inhibits their ability to explain how they came to their conclusion, creating distrust between DL systems and radiologists. This research employs two CNN model architectures - AlexNet and Houby & Yassin's CNN - to classify recently obtained mammographic images from the KAU-BCMD dataset into BI-RAD categories (0-6). To resolve the problem of non-explainability, two variants of SHapley Additive exPlanations (SHAP), an explainability approach based on Shapley values, have been used to assess the role that each feature has in the prediction process.