Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1239 - 1251 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143136818&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143136818 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2020 vid: 58 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143136818 143136818 NLM32221796 10.1007/s11517-020-02159-z NLM32221796 143136818 ppf: 1239 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. aug: au: Li, Zongyao Togo, Ren Ogawa, Takahiro Haseyama, Miki affil: Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, 060-0814, Sapporo, Japan sug: subj: Gastritis Radiography Methods Image Processing, Computer Assisted Methods Gastritis Classification Resource Databases Chronic Disease Diagnosis, Computer Assisted Ferrans and Powers Quality of Life Index ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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