DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
Jimmy Wu, Bolei Zhou, Diondra Peck, Scott S. Hsieh, Vandana M. Dialani, Lester Mackey, Geneviève Patterson · Harvard Data Science Review · 2021
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Digital Database for Screening Mammography. We show that DeepMiner not only enables better understanding of the nuances of CNN classification decisions but also possibly discovers new visual knowledge relevant to medical diagnosis.