A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease

Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data in medical image analysis. In the first phase, the model learns useful visual features from a large collection of unlabeled retinal images using self-supervised training. In the second phase, it is fine-tuned on a smaller set of labeled images for the classification of RP and STGD. Experimental results demonstrate that using 5844 unlabeled and 782 labeled fundus images showed that our method, based on the EfficientNet-B1 architecture, outperforms state-of-the-art supervised learning methods, achieving 98.15% accuracy and 99.68% AUC. The proposed method is flexible and scalable, making it well-suited for real-world applications where labeled data is scarce.

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Publication Details

Journal
Scientific Reports
Published
2025-12-08
DOI
https://doi.org/10.1038/s41598-025-27367-1
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease

Hamid Ahmadieh, Masoud Yazdanpanah, Narsis Daftarian, Sahba Fekri et al.
Scientific Reports
Retinal Imaging and Analysis
article

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease

Hamid Ahmadieh, Masoud Yazdanpanah, Narsis Daftarian, Sahba Fekri, Hadi Ghattan Kashani, Hamideh Sabbaghi, Ali Abbasi, Masoud Shariat Panahi, Ali Karimi, Melika Sadeghi Tabrizi, Hemn Baghban Jaldian
article en

Abstract

Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data in medical image analysis. In the first phase, the model learns useful visual features from a large collection of unlabeled retinal images using self-supervised training. In the second phase, it is fine-tuned on a smaller set of labeled images for the classification of RP and STGD. Experimental results demonstrate that using 5844 unlabeled and 782 labeled fundus images showed that our method, based on the EfficientNet-B1 architecture, outperforms state-of-the-art supervised learning methods, achieving 98.15% accuracy and 99.68% AUC. The proposed method is flexible and scalable, making it well-suited for real-world applications where labeled data is scarce.

Scientific ReportsVol. 15(1)
University of British Columbia (CA), Amirkabir University of Technology (IR), University of Tehran (IR), Research Institute for Endocrine Sciences (IR), Shahid Beheshti University of Medical Sciences (IR)
Shahid Beheshti University of Medical Sciences
Openalex Percentile: Top 46%
Retinal Imaging and Analysis
DOIOpen Access PDFOpenAlex
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