open access

https://hdl.handle.net/2115/81642

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

英語

Semi-supervised learning

2020-06

Springer

Medical & Biological Engineering & Computing

58(6)

1239-1250

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.

学術雑誌論文

Accepted Manuscript

This is a post-peer-review, pre-copyedit version of an article published in Medical & Biological Engineering & Computinginsert. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11517-020-02159-z.

0140-0118

AA00726781