  Automated diagnosis using deep neural networks in chest radiography can help
radiologists detect life-threatening diseases. However, existing methods only
provide predictions without accurate explanations, undermining the
trustworthiness of the diagnostic methods. Here, we present XProtoNet, a
globally and locally interpretable diagnosis framework for chest radiography.
XProtoNet learns representative patterns of each disease from X-ray images,
which are prototypes, and makes a diagnosis on a given X-ray image based on the
patterns. It predicts the area where a sign of the disease is likely to appear
and compares the features in the predicted area with the prototypes. It can
provide a global explanation, the prototype, and a local explanation, how the
prototype contributes to the prediction of a single image. Despite the
constraint for interpretability, XProtoNet achieves state-of-the-art
classification performance on the public NIH chest X-ray dataset.
