XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
By Eunji Kim, Siwon Kim, Minji Seo, Sungroh Yoon. 2021
147 citations recorded by the source corpus.
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.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization (2021, methodology): “Case-based reasoning models have been developed for different problems in radiology using interpretable ML [60, 61], and thus such methods would be poised for use in embryology.”
- Decoupling Deep Learning for Interpretable Image Recognition (2022, methodology): “ProtoPNet [3], Gen-ProtoPNet [25], and XProtoNet [13] use patches of different sizes in feature maps as prototypes for classification.”