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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Sp-cpgcn: causality-perception graph convolutional network on superpixel prior for pneumoconiosis staging

Wang Yueying1a
Ji Guohua1a
Feng Weiyi1a
Zhao Juanjuan1a,1b
Qiang Yan1a,2
Ma Jianfen1a
Shi Yiwei3
Yang Fan4
1. a. College of Computer Science & Technology, b. School of Software, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
2. School of Software, North University of China, Taiyuan Shanxi 030051, China
3. NHC Key Laboratory of Pneumoconiosis; Shanxi Key Laboratory of Respiratory Diseases; Dept. of Pulmonary & Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan Shanxi 030012, China
4. Occupational Disease Prevention & Control Center of Xishan Coal & Electricity (Group) Company, Taiyuan Shanxi 030053, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0352

Abstract

To address the low accuracy in pneumoconiosis staging caused by the small and thin pneumoconiosis foci and the influence of non-causal features in existing deep learning methods, this paper proposed a causality-perception graph convolutional network on superpixel priors (SP-CPGCN) . The method extracted local, subtle features by performing feature extraction on superpixels rather than the entire chest radiograph. The method constructed a graph network by fully considering the spatial proximity and feature similarity among nodes, and designed a hierarchical aggregated graph convolutional network to enable information transfer and feature integration across different depths. Additionally, the method employed adaptive causal inference on graph convolutional networks, using a causal intervention strategy that combines intervention loss and stability loss to avoid the interference of non-causal features. It also introduced an intraclass consistency loss to balance individual-specific features with group-universal features. The validation results on a clinical pneumoconiosis chest radiograph dataset show that SP-CPGCN achieved an accuracy of 82.4%, a precision of 78.9%, a sensitivity of 77.3%, a specificity of 88.6%, and an AUC of 90.3%, outperforming other methods. The experimental results show that SP-CPGCN effectively improves the accuracy and stability of pneumoconiosis staging and provides a reliable new method for automated medical diagnosis.

Foundation Support

国家自然科学基金资助项目(U21A20469,62376183)
中央引导地方科技发展基金资助项目(YDZJSX2022C004)
山西省基础研究计划项目(202103021224066)
国家卫生健康委尘肺病重点实验室开放课题(YKFKT004)
山西省科技创新人才团队专项计划(202304051001009)
国家卫生健康委尘肺病重点实验室(2020-PT320-005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0352
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-03-06] Accepted Paper

Cite This Article

王月莹, 纪国华, 冯伟毅, 等. SP-CPGCN：用于尘肺病分期的超像素先验因果感知图卷积网络 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0352. (Wang Yueying, Ji Guohua, Feng Weiyi, et al. Sp-cpgcn: causality-perception graph convolutional network on superpixel prior for pneumoconiosis staging [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0352. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
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