XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations

• URL: http://arxiv.org/abs/2103.10663v1
• Date: Fri, 19 Mar 2021 07:18:21 GMT
• Title: XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
• Authors: Eunji Kim, Siwon Kim, Minji Seo, Sungroh Yoon
• Abstract summary: 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. It can provide a global explanation, the prototype, and a local explanation, how the prototype contributes to the prediction of a single image.
• Score: 19.71623263373982
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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