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Self-supervised learning for gastritis detection with gastric X-ray images
Guang Li1, Ren Togo2, Takahiro Ogawa2
1Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
A new self-supervised learning method effectively detects gastritis from gastric X-ray images using minimal annotated data. This approach significantly outperforms existing methods, offering a promising tool for clinical diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer-Aided Diagnosis
Background:
- Manual annotation of gastric X-ray images for gastritis detection is labor-intensive and costly.
- Developing automated methods is crucial for efficient and accurate diagnosis.
Purpose of the Study:
- To develop and validate a novel self-supervised learning (SSL) method for gastritis detection using gastric X-ray images.
- To assess the method's effectiveness with limited annotated data.
Main Methods:
- A novel SSL method utilizing a teacher-student architecture was developed to learn discriminative representations from gastric X-ray images.
- The proposed method was fine-tuned on small datasets of annotated images and compared against five SSL techniques and three baseline methods.
Main Results:
- The proposed method achieved high performance, with harmonic mean scores of sensitivity and specificity ranging from 0.875 to 0.931 as annotated data increased.
- It outperformed all compared SSL and traditional methods, demonstrating its efficacy in gastritis detection with few annotations.
Conclusions:
- A novel SSL method based on a teacher-student architecture shows significant potential for clinical application in gastritis detection.
- The method effectively learns from limited annotated gastric X-ray data, addressing the limitations of manual annotation.
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