Radiologist-inspired Symmetric Local–Global Multi-Supervised Learning for early diagnosis of pneumoconiosis

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Expert Systems with Applications Pub Date : 2025-03-12 DOI:10.1016/j.eswa.2025.127173
Jiarui Wang , Meiyue Song , Deng-Ping Fan , Xiaoxu Wang , Shaoting Zhang , Juntao Yang , Jiangfeng Liu , Chen Wang , Binglu Wang
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Abstract

Pneumoconiosis is a severe occupational lung disease caused by long-term exposure to inhaled dust, where early diagnosis is critical for effective management and health protection. However, current deep learning approaches struggle with the subtle radiographic manifestations of pneumoconiosis, strict diagnostic criteria, and limited data availability. In this paper, we propose Symmetric Local–Global Multi-Supervised Learning (SLGMS), a novel framework inspired by the diagnostic practices of specialized radiologists. SLGMS integrates a mechanism for generating symmetric global and local views with a symmetric VMamba feature extraction network, effectively mimicking the region-by-region analysis and comparative assessment of symmetric regions performed by radiologists. Additionally, it incorporates a local–global knowledge distillation architecture with tailored multi-supervised learning to explore relationships between local and global views while adhering to clinical diagnostic criteria for pneumoconiosis. Evaluated on pneumoconiosis datasets collected from two medical hospitals in China, SLGMS demonstrates superior performance, achieving an average improvement of 6.19% in accuracy, sensitivity, specificity, and AUC metrics on the internal test set and 3.28% on the external validation dataset compared to state-of-the-art methods. On the public NIH ChestX-ray14 benchmark, a transferable variant of SLGMS achieved a new state-of-the-art AUC of 82.9%, while the full SLGMS provides an average improvement of 3.5% on its supplemental fibrosis dataset. By bridging diagnostic prior knowledge with deep learning, SLGMS offers an effective paradigm for early diagnosis of occupational pneumoconiosis in data-scarce environments, with broader applicability and scalability to other thoracic diseases.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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