Self-supervised patient-specific features learning for OCT image classification.
Deep learning's great success in image classification is heavily reliant on large-scale annotated datasets. However, obtaining labels for optical coherence tomography (OCT) data requires the significant effort of professional ophthalmologists, which hinders the application of deep learning in OCT im...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 10; pp. 2851 - 2864 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2022
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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=159003922&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159003922 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2022 vid: 60 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159003922 158359846 159003922 NLM35931872 10.1007/s11517-022-02627-8 NLM35931872 159003922 ppf: 2851 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Self-supervised patient-specific features learning for OCT image classification. aug: au: Fang, Leyuan Guo, Jiahuan He, Xingxin Li, Muxing affil: The College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China sug: subj: Tomography, Optical Coherence Methods ab: Deep learning's great success in image classification is heavily reliant on large-scale annotated datasets. However, obtaining labels for optical coherence tomography (OCT) data requires the significant effort of professional ophthalmologists, which hinders the application of deep learning in OCT image classification. In this paper, we propose a self-supervised patient-specific features learning (SSPSF) method to reduce the amount of data required for well OCT image classification results. Specifically, the SSPSF consists of a self-supervised learning phase and a downstream OCT image classification learning phase. The self-supervised learning phase contains two self-supervised patient-specific features learning tasks. One is to learn to discriminate an OCT scan which belongs to a specific patient. The other task is to learn the invariant features related to patients. In addition, our proposed self-supervised learning model can learn inherent representations from the OCT images without any manual labels, which provides well initialization parameters for the downstream OCT image classification model. The proposed SSPSF achieves classification accuracy of 97.74% and 98.94% on the public RETOUCH dataset and AI Challenger dataset, respectively. The experimental results on two public OCT datasets show the effectiveness of the proposed method compared with other well-known OCT image classification methods with less annotated data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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