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Patients undergoing chest X-rays (CXR) often endure multiple lung diseases.When evaluating a patient's condition, due to the complex pathologies, subtletexture changes of different lung lesions in images, and patient conditiondifferences, radiologists may make uncertain even when they have experiencedlong-term clinical training and professional guidance, which makes much noisein extracting disease labels based on CXR reports. In this paper, we re-extractdisease labels from CXR reports to make them more realistic by consideringdisease severity and uncertainty in classification. Our contributions are asfollows: 1. We re-extracted the disease labels with severity and uncertainty bya rule-based approach with keywords discussed with clinical experts. 2. Tofurther improve the explainability of chest X-ray diagnosis, we designed amulti-relationship graph learning method with an expert uncertainty-aware lossfunction. 3. Our multi-relationship graph learning method can also interpretthe disease classification results. Our experimental results show that modelsconsidering disease severity and uncertainty outperform previousstate-of-the-art methods.

Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning