Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

查看全文
  • Ali Karimi,
  • Hamid Ahmadieh,
  • Melika Sadeghi Tabrizi,
  • Narsis Daftarian,
  • Ali Farshian Abbasi,
  • Sahba Fekri,
  • Hadi Ghattan Kashani,
  • Hemn Baghban Jaldian,
  • Mahdi Yazdanpanah,
  • Masoud Shariat Panahi,
  • Hamideh Sabbaghi

摘要

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.