Fine Annotation Loss and Top-k Analysis in Interpretable Models for Breast Cancer Prediction
Mammograms provide critical information to radiologists, aiding in the early detection of cancer. We would like to use neural network models to assist radiologists in this challenging and important task, however, these algorithms are “black box” – unable to explain their reasoning to humans. Because of their opacity, neural networks can hide flawed reasoning while appearing to perform well. To address this, we create human-interpretable models that provide step-by-step reasoning for their predictions. Improving on previous work, we extend from three to all five BI-RADS mass margin categories. We also provide the only in-depth evaluation of how two essential changes to the algorithm (fine annotation loss, top-k activation) impact the performance of these interpretable models.
