A multi-label chest X-ray image classification algorithm based on multi-scale and attribute-aware semantic graph

• Published in: Expert systems with applications Vol. 298; p. 129898
• Main Authors: Wang, Qian, Wu, Zhijuan, Gao, Jiyu, Yu, Hongnian, Cheng, Yongqiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2026
• Subjects: Chest X-ray; Graph convolutional network; Medical image analysis; Multi-label classification; Transformer; Graph convolutional network; Transformer; Chest X-ray; Multi-label classification; Medical image analysis
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2025.129898

•A multi-scale feature partitioning method is proposed to improve lesion recognition.•A label-guided alignment method is designed to enhance visual-semantic consistency.•An attribute-aware graph method is proposed to better capture label dependencies. [Display omitted] Multi-label Chest X-Ray classification is crucial for intelligent diagnosis, yet existing algorithms usually ignore lesion-scale heterogeneity and attribute-conditioned label dependencies, limiting their clinical generalizability. To address these issues, this paper proposes MSASG, a multi-label Chest X-Ray image classification algorithm based on Multi-Scale and Attribute-aware Semantic Graph which enhances discriminative power and semantic consistency. Firstly, a Multi-scale Feature Partitioning and Reconstruction method is proposed to capture lesion patterns at different scales. Secondly, a Label-guided Multi-scale Semantic Alignment method is proposed to improve visual-semantic alignment by integrating label embeddings into feature extraction and using a Transformer to model high-order cross-modal dependencies. Finally, an Attribute-aware Graph Convolutional Network method is proposed to construct attribute-specific label co-occurrence matrices and dynamically select relevant structures during inference, enabling personalized characterization of label dependencies. Experiments on ChestX-ray14 and CheXpert show that MSASG outperforms state-of-the-art methods in recognizing complex lesion co-occurrence and adapting to heterogeneous populations.