A Multitask Network for the Diagnosis of Autoimmune Gastritis.

Journal: Journal of imaging
Published Date: May 15, 2025

Abstract

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by () infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. Therefore, when diagnosing AIG based on endoscopic images, it is essential not only to distinguish between normal and atrophic gastric mucosa but also to accurately identify the anatomical region in which the atrophic mucosa is located. In this study, we propose a patient-based multitask gastroscopy image classification network that analyzes all images obtained during the endoscopic procedure. First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. Next, we use a hierarchical clustering algorithm to group images based on this matrix. Finally, we apply the RepLKNet model, which utilizes large-kernel convolution, to each image group to perform two tasks: anatomical region classification and lesion recognition. Our method achieves an accuracy of 93.4 ± 0.5% (95% CI) and a precision of 92.6 ± 0.4% (95% CI) in the anatomical region classification task, which categorizes images into the fundus, body, and antrum. Additionally, it attains an accuracy of 90.2 ± 1.0% (95% CI) and a precision of 90.5 ± 0.8% (95% CI) in the lesion recognition task, which identifies the presence of gastric mucosal atrophic lesions in gastroscopy images. These results demonstrate that the proposed multitask patient-based gastroscopy image analysis method holds significant practical value for advancing computer-aided diagnosis systems for atrophic gastritis and enhancing the diagnostic accuracy and efficiency of AIG.

Authors

  • Yuqi Cao
    State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Yining Zhao
  • Xinao Jin
    State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Jiayuan Zhang
    State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Gangzhi Zhang
    Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.
  • Pingjie Huang
    State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China. huangpingjie@zju.edu.cn.
  • Guangxin Zhang
    State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China. gxzhang@zju.edu.cn.
  • Yuehua Han
    Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.

Keywords

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