Label Semantic Improvement with Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification

Dachuan Cai, Huijuan Lu, Zhuijun Chai, Renfeng Wang, Wenjie Zhu, Yudong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multi-label classification of Chest X-ray images is one of the important tasks in the field of medical image analysis, and current methods only capture co-occurrence relationships between labels, making it difficult to establish complex connections among labels. We face a series of challenges when considering global label correlation, visual correlation, and dealing with unbalanced data. In this study, we introduce a Label Semantic Improvement Graph Convolutional Network (LSI-GCN) framework based on GCN, which consists of an Image Representation Learning module, a GCN module with Label Semantic Improvement, and a Distance Metric module to improve classification performance. The task of multi-label classification of chest X-ray images is approached from different perspectives and levels by combining their functionalities to improve the performance, robustness, and accuracy of the model. The Image Representation Learning module is a feature extractor that learns high-level label-specific features from chest X-ray images. The GCN captures label correlations and models global label relationships and category visual correlations, respectively. With this approach, the correlation between disease labels and the relevance of image features can be considered simultaneously, and the accuracy of multi-label classification can be improved. The image features were further optimized using a distance metric module. Experiments were conducted using Chest X-ray14 and CheXpert images as multi-label datasets, and the mean Area Under Curve (AUC) scores obtained were better than those of comparative models such as CheXGCN [3] and the accuracy and robustness of multi-label classification were improved.

Original languageEnglish
Title of host publicationProceedings - 2023 13th International Conference on Information Technology in Medicine and Education, ITME 2023
Pages711-717
Number of pages7
ISBN (Electronic)9798350319156
DOIs
StatePublished - 2023
Event13th International Conference on Information Technology in Medicine and Education, ITME 2023 - Wuyishan, China
Duration: 24 Nov 202326 Nov 2023

Publication series

NameProceedings - 2023 13th International Conference on Information Technology in Medicine and Education, ITME 2023

Conference

Conference13th International Conference on Information Technology in Medicine and Education, ITME 2023
Country/TerritoryChina
CityWuyishan
Period24/11/2326/11/23

Keywords

  • Chest X-ray
  • Graph Convolutional Networks
  • Image classification
  • Multi-label

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