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

TL;DR

This paper introduces a novel multi-relationship graph learning approach for chest X-ray classification that incorporates disease severity and uncertainty, improving interpretability and performance over existing methods.

Contribution

It re-extracts disease labels considering severity and uncertainty, and proposes a multi-relationship graph learning method with an expert uncertainty-aware loss for better diagnosis and explanation.

Findings

Models with severity and uncertainty outperform previous methods.

The approach enhances interpretability of disease classification.

Re-extracted labels improve training realism.

Abstract

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.