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.

中文翻译：

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使用基于X射线训练的半监督学习方法使用胃部X射线图像对慢性胃炎进行分类。

用于医学图像的高质量注释总是昂贵且稀缺的。深度学习在医学图像分析领域的许多应用都面临着注释数据不足的问题。在本文中，我们提出了一种使用胃X射线图像对慢性胃炎进行分类的半监督学习方法。所提出的基于三级训练的半监督学习方法可以利用未注释的数据来提高通过少量注释数据获得的性能。我们利用一种称为“班际学习”（BC Learning）的新颖学习方法，可以大大提高我们的半监督学习方法的性能。结果，我们的方法可以有效地从未注释的数据中学习，并实现对慢性胃炎的高诊断准确性。