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Found 2 papers, 1 papers with code
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.
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