# Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images

arXiv: https://arxiv.org/abs/2104.02864 · HTML: https://mlsanity.com/paper/2104.02864/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 56 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Understanding self-supervised Learning Dynamics without Contrastive Pairs](https://mlsanity.com/paper/2102.06810/) (2021)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Understanding Self-supervised Learning with Dual Deep Networks](https://mlsanity.com/paper/2010.00578/) (2020)
- [Multimodal Self-Supervised Learning for Medical Image Analysis](https://mlsanity.com/paper/1912.05396/) (2019)
- [Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images](https://mlsanity.com/paper/2103.03423/) (2021)

## Built on by (most cited first, 9 of 9)

- [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://mlsanity.com/paper/2209.14635/) (2022)
- [Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images](https://mlsanity.com/paper/2206.03009/) (2022)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images](https://mlsanity.com/paper/2212.09276/) (2022)
- [Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images](https://mlsanity.com/paper/2501.04217/) (2025)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness](https://mlsanity.com/paper/2510.27213/) (2025)
- [Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum](https://mlsanity.com/paper/2209.14743/) (2022)
- [Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification](https://mlsanity.com/paper/2508.20461/) (2025)

## Abstract (arXiv)

Purpose: Manual 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. Methods: In 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. Results: The 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. Conclusions: This 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.