Expert Uncertainty and Severity Aware Chest X-Ray Classification by
  Multi-Relationship Graph Learning
Mengliang Zhang Xinyue Hu Lin Gu Liangchen Liu Kazuma Kobayashi Tatsuya Harada Ronald M. Summers Yingying Zhu
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.