ArticleMedical & biological engineering & computing2020
Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Estimating the Amount of Air Inside the Stomach for Detecting Cancers on Gastric Radiographs Using Artificial Intelligence: an Observational, Cross-sectional Study.Journal of imaging informatics in medicine · 2025Observational
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Self-supervised learning for gastritis detection with gastric X-ray images.International journal of computer assisted radiology and surgery · 2023Article
- Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation.Entropy (Basel, Switzerland) · 2023Article
- Research on Anomaly Identification and Screening and Metallogenic Prediction Based on Semisupervised Neural Network.Computational intelligence and neuroscience · 2022Article
- Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients.BioMed research international · 2022Article
- Deep learning-based facial image analysis in medical research: a systematic review protocol.BMJ open · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
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. Graphical Abstract Gastritis classification using gastric X-ray images with semi-supervised learning.
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Registered trials
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