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Self-supervised learning for gastritis detection with gastric X-ray images
| Title: | Self-supervised learning for gastritis detection with gastric X-ray images |
| Authors: | Li, Guang Browse this author | | Togo, Ren Browse this author →KAKEN DB | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Deep learning | | Medical image analysis | | Gastric X-ray examination | | Self-supervised learning |
| Issue Date: | 11-Apr-2023 |
| Publisher: | Springer |
| Journal Title: | International Journal of Computer Assisted Radiology and Surgery |
| Volume: | 18 |
| Start Page: | 1841 |
| End Page: | 1848 |
| Publisher DOI: | 10.1007/s11548-023-02891-5 |
| Abstract: | PurposeManual annotation of gastric X-ray images by doctors for gastritis detection is time-consuming and expensive. To solve this, a self-supervised learning method is developed in this study. The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images.MethodsIn this study, we develop a novel method that can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. Models trained based on the proposed method were fine-tuned on datasets comprising a few annotated gastric X-ray images. Five self-supervised learning methods, i.e., SimSiam, BYOL, PIRL-jigsaw, PIRL-rotation, and SimCLR, were compared with the proposed method. Furthermore, three previous methods, one pretrained on ImageNet, one trained from scratch, and one semi-supervised learning method, were compared with the proposed method.ResultsThe proposed method's harmonic mean score of sensitivity and specificity after fine-tuning with the annotated data of 10, 20, 30, and 40 patients were 0.875, 0.911, 0.915, and 0.931, respectively. The proposed method outperformed all comparative methods, including the five self-supervised learning and three previous methods. Experimental results showed the effectiveness of the proposed method in gastritis detection using a few annotated gastric X-ray images.ConclusionsThis paper proposes a novel self-supervised learning method based on a teacher-student architecture for gastritis detection using gastric X-ray images. The proposed method can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. The proposed method exhibits potential clinical use in gastritis detection using a few annotated gastric X-ray images. |
| Rights: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11548-023-02891-5 |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/91736 |
| Appears in Collections: | 数理・データサイエンス教育研究センター (Education and Research Center for Mathematical and Data Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: Li Guang(李 広)
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