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Open Access

KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection

School of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
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Abstract

Automated diagnosis of chest X-rays is pivotal in radiology, aiming to alleviate the workload of radiologists. Traditional methods primarily rely on visual features or label dependence, which is a limitation in detecting nuanced or rare lesions. To address this, we present KEXNet, a pioneering knowledge-enhanced X-ray lesion detection model. KEXNet employs a unique strategy akin to expert radiologists, integrating a knowledge graph based on expert annotations with an interpretable graph learning approach. This novel method combines object detection with a graph neural network, facilitating precise local lesion detection. For global lesion detection, KEXNet synergizes knowledge-enhanced local features with global image features, enhancing diagnostic accuracy. Our evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models, particularly in identifying small or infrequent lesions. Notably, on the Chest ImaGenome dataset, KEXNet’s AUC for local lesion detection surpasses 8.9% compared to the state-of-the-art method AnaXNet, showcasing its potential in revolutionizing automated chest X-ray diagnostics.

Keywords

multi-label chest X-ray classificationobject detectionknowledge graph learning

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Big Data Mining and Analytics
Volume 7 Issue 4,
December 2024
Pages 1187-1198
DOI: 10.26599/BDMA.2024.9020045
Cite this article:
Yan Q, Duan J, Wang J. KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection. Big Data Mining and Analytics, 2024, 7(4): 1187-1198. https://doi.org/10.26599/BDMA.2024.9020045

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Received: 26 January 2024
Revised: 20 May 2024
Accepted: 03 June 2024
Published: 04 December 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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