Breast density classification and decision explainability by deep sparse approximations

Breast density, defined as the proportion of breast tissue composed of dense fibroglandular tissue, is a crucial factor in assessing breast cancer risk and significantly impacts the visibility of lesions during mammography screening. We focus on distinguishing between high and low breast density by fine-tuning deep neural networks and on our joint deep and sparse approximation methodology. We evaluate the performance of these approaches in classifying high versus low breast density. Furthermore, we propose an example-based explainable AI approach denoted as Deep Sparse Reconstruct (DSR), which visualizes the most influential deep features in the corresponding mammogram region of interest, identified by non-negative sparse representations derived from a training dictionary. We also utilize established explainable AI techniques that visualize model predictions to facilitate comparisons. Our findings support that DSR enhances interpretability by extracting the key training features that contribute to predictions, it is compatible with various deep network architectures, and may contribute towards the development of trustworthy AI diagnostic workflows.