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Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training


High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a novel learning method named Between-Class learning (BC learning) that can considerably enhance the performance of our semi-supervised learning method. As a result, our method can effectively learn from unannotated data and achieve high diagnostic accuracy for chronic gastritis.

Gastritis classification using gastric X-ray images with semi-supervised learning.

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The clinical data were acquired at The University of Tokyo Hospital in Japan. We express our thanks to Katsuhiro Mabe of the Junpukai Health Maintenance Center and Nobutake Yamamichi of The University of Tokyo Hospital.


This study was partly supported by JSPS KAKENHI Grant number JP17H01744.

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Correspondence to Zongyao Li.

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Li, Z., Togo, R., Ogawa, T. et al. Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Med Biol Eng Comput 58, 1239–1250 (2020).

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  • Chronic gastritis
  • Computer-aided diagnosis
  • Medical image analysis
  • Convolutional neural network
  • Semi-supervised learning