A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification.
Diabetic retinopathy (DR) is a retinal disease caused by diabetes. If there is no intervention, it may even lead to blindness. Therefore, the detection of diabetic retinopathy is of great significance for preventing blindness in patients. Most of the existing DR detection methods use supervised meth...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1796 - 1812 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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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=185280514&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280514 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280514 185280514 185280514 10.1007/s10278-024-01270-z 185280514 ppf: 1796 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification. aug: au: Fan, Jiacheng Yang, Tiejun Wang, Heng Zhang, Huiyao Zhang, Wenjie Ji, Mingzhu Miao, Jianyu affil: https://ror.org/05sbgwt55 School of Information Science and Engineering, Henan University of Technology, 450001, Zhengzhou, China sug: subj: Diabetic Retinopathy Classification Self Care Deep Learning Image Interpretation, Computer Assisted Human Funding Source Blindness Prevention and Control Neural Networks (Computer) Learning Methods Supervisors and Supervision Diagnosis, Computer Assisted Uncertainty Machine Learning Algorithms ab: Diabetic retinopathy (DR) is a retinal disease caused by diabetes. If there is no intervention, it may even lead to blindness. Therefore, the detection of diabetic retinopathy is of great significance for preventing blindness in patients. Most of the existing DR detection methods use supervised methods, which usually require a large number of accurate pixel-level annotations. To solve this problem, we propose a self-supervised Equivariant Refinement Classification Network (ERCN) for DR classification. First, we use an unsupervised contrast pre-training network to learn a more generalized representation. Secondly, the class activation map (CAM) is refined by self-supervision learning. It first uses a spatial masking method to suppress low-confidence predictions, and then uses the feature similarity between pixels to encourage fine-grained activation to achieve more accurate positioning of the lesion. We propose a hybrid equivariant regularization loss to alleviate the degradation caused by the local minimum in the CAM refinement process. To further improve the classification accuracy, we propose an attention-based multi-instance learning (MIL), which weights each element of the feature map as an instance, which is more effective than the traditional patch-based instance extraction method. We evaluate our method on the EyePACS and DAVIS datasets and achieved 87.4% test accuracy in the EyePACS dataset and 88.7% test accuracy in the DAVIS dataset. It shows that the proposed method achieves better performance in DR detection compared with other state-of-the-art methods in self-supervised DR detection. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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