Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning

TL;DR

This paper introduces the Cross-chest Graph (CCG), a novel method that models intra- and inter-image relationships to improve weakly-supervised lesion localization in X-ray images, mimicking doctors' diagnostic habits.

Contribution

The paper proposes the CCG framework that incorporates structural relational reasoning to enhance weakly-supervised disease localization in X-ray images, addressing limitations of previous methods.

Findings

Achieves state-of-the-art performance on NIH Chest-14 dataset.

Effectively models intra-image anatomical relationships.

Utilizes inter-image comparison to improve lesion detection.

Abstract

Locating lesions is important in the computer-aided diagnosis of X-ray images. However, box-level annotation is time-consuming and laborious. How to locate lesions accurately with few, or even without careful annotations is an urgent problem. Although several works have approached this problem with weakly-supervised methods, the performance needs to be improved. One obstacle is that general weakly-supervised methods have failed to consider the characteristics of X-ray images, such as the highly-structural attribute. We therefore propose the Cross-chest Graph (CCG), which improves the performance of automatic lesion detection by imitating doctor's training and decision-making process. CCG models the intra-image relationship between different anatomical areas by leveraging the structural information to simulate the doctor's habit of observing different areas. Meanwhile, the relationship between any pair of images is modeled by a knowledge-reasoning module to simulate the doctor's habit of comparing multiple images. We integrate intra-image and inter-image information into a unified end-to-end framework. Experimental results on the NIH Chest-14 database (112,120 frontal-view X-ray images with 14 diseases) demonstrate that the proposed method achieves state-of-the-art performance in weakly-supervised localization of lesions by absorbing professional knowledge in the medical field.