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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training
| Title: | Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training |
| Authors: | Li, Zongyao Browse this author | | Togo, Ren Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Chronic gastritis | | Computer-aided diagnosis | | Medical image analysis | | Convolutional neural network | | Semi-supervised learning |
| Issue Date: | Jun-2020 |
| Publisher: | Springer |
| Journal Title: | Medical & Biological Engineering & Computing |
| Volume: | 58 |
| Issue: | 6 |
| Start Page: | 1239 |
| End Page: | 1250 |
| Publisher DOI: | 10.1007/s11517-020-02159-z |
| 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. |
| Rights: | 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. |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/81642 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小川 貴弘
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