Paper Abstract and Keywords |
Presentation |
2022-02-21 13:30
A Note on Automatic Diagnosis of Helicobacter Pylori Infection Based on Self-Supervised Learning and Self-Knowledge Distillation Guang Li, Ren Togo (Hokkaido Univ.), Katsuhiro Mabe (Junpukai Health Maintenance Center), Shunpei Nishida (Olympus), Yoshihiro Tomoda (Olympus Medical Systems), Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This paper proposes a novel method for automatic diagnosis of Helicobacter pylori (H. pylori) infection based on self-supervised learning and self-knowledge distillation. Our method consists of two phases, the first is the self-supervised learning phase for learning discriminative representations from gastric endoscopic images, and the second is the self-knowledge distillation based fine-tuning phase for accurate automatic diagnosis of H. pylori infection. Our method achieves high diagnosis performance in a complex endoscopic image dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Helicobacter pylori infection / endoscopic image / self-supervised learning / self-knowledge distillation / / / / |
Reference Info. |
ITE Tech. Rep., vol. 46, no. 6, ME2022-34, pp. 49-52, Feb. 2022. |
Paper # |
ME2022-34 |
Date of Issue |
2022-02-14 (MMS, ME, AIT) |
ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
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