2024-03-29T05:44:41Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/816422022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-trainingLi, ZongyaoTogo, Ren1000020524028Ogawa, Takahiro1000000218463Haseyama, Mikiopen accessThis 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.Chronic gastritisComputer-aided diagnosisMedical image analysisConvolutional neural networkSemi-supervised learning540High-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.Springer2020-06engjournal articleAMhttp://hdl.handle.net/2115/81642https://doi.org/10.1007/s11517-020-02159-z0140-0118AA00726781Medical & Biological Engineering & Computing58612391250https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/81642/1/manuscript-1.pdfapplication/pdf7.12 MB2020-06