XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
This addresses the need for trustworthy diagnostic methods in chest radiography for radiologists, though it is incremental as it builds on existing interpretability approaches.
The paper tackled the problem of automated diagnosis in chest radiography lacking accurate explanations by proposing XProtoNet, a framework that provides global and local interpretability using prototypes, and it achieved state-of-the-art classification performance on the NIH chest X-ray dataset.
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.