Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
This work addresses label noise in medical imaging for radiologists and AI systems, but it is incremental as it builds on existing graph learning approaches.
The paper tackled the problem of noisy disease labels in chest X-ray classification by re-extracting labels with severity and uncertainty, and introduced a multi-relationship graph learning method with an expert uncertainty-aware loss function, resulting in models that outperform previous state-of-the-art methods.
Patients undergoing chest X-rays (CXR) often endure multiple lung diseases. When evaluating a patient's condition, due to the complex pathologies, subtle texture changes of different lung lesions in images, and patient condition differences, radiologists may make uncertain even when they have experienced long-term clinical training and professional guidance, which makes much noise in extracting disease labels based on CXR reports. In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our contributions are as follows: 1. We re-extracted the disease labels with severity and uncertainty by a rule-based approach with keywords discussed with clinical experts. 2. To further improve the explainability of chest X-ray diagnosis, we designed a multi-relationship graph learning method with an expert uncertainty-aware loss function. 3. Our multi-relationship graph learning method can also interpret the disease classification results. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.