Scholarlink

<p>This study quantitatively assesses the relative contribution of anatomically defined retinal regions (macula, optic disc, and retinal vasculature) to biological sex classification from fundus photographs. We developed a two-stage multi-branch framework using pre-trained ResNet50 backbones for region-specific feature extraction and an attention-based fusion module that produces ROI-level scalar weights. The dataset comprised 3,478 eye-level fundus images with a balanced sex distribution, and data were split on a subject-wise basis to avoid information leakage between sets. The fusion model achieved the highest discriminative performance (AUC = 0.861; 95% CI: 0.826–0.895). Single-branch results suggested complementary tendencies, with the macula branch showing higher sensitivity for female classification and the optic disc branch showing higher specificity for male classification. In the fusion model, mean attention weights for the macula (0.408; 95% CI: 0.369–0.446) and optic disc (0.383; 95% CI: 0.347–0.418) were comparable (<i>p</i> = 0.506), and both were significantly higher than those for the vasculature (0.209; 95% CI: 0.184–0.238; <i>p</i> &lt; 0.001). These region-level weights provide a reproducible, quantitative estimate of model reliance across predefined ROIs and complement qualitative explainability methods. Overall, the proposed framework supports more transparent and anatomically interpretable AI for fundus-based sex classification.</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

Quantifying regional contributions to sex classification from fundus photographs via a two-stage attention-based deep learning approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

查看全文
  • Joseph Kim,
  • Shina Jang

摘要

This study quantitatively assesses the relative contribution of anatomically defined retinal regions (macula, optic disc, and retinal vasculature) to biological sex classification from fundus photographs. We developed a two-stage multi-branch framework using pre-trained ResNet50 backbones for region-specific feature extraction and an attention-based fusion module that produces ROI-level scalar weights. The dataset comprised 3,478 eye-level fundus images with a balanced sex distribution, and data were split on a subject-wise basis to avoid information leakage between sets. The fusion model achieved the highest discriminative performance (AUC = 0.861; 95% CI: 0.826–0.895). Single-branch results suggested complementary tendencies, with the macula branch showing higher sensitivity for female classification and the optic disc branch showing higher specificity for male classification. In the fusion model, mean attention weights for the macula (0.408; 95% CI: 0.369–0.446) and optic disc (0.383; 95% CI: 0.347–0.418) were comparable (p = 0.506), and both were significantly higher than those for the vasculature (0.209; 95% CI: 0.184–0.238; p < 0.001). These region-level weights provide a reproducible, quantitative estimate of model reliance across predefined ROIs and complement qualitative explainability methods. Overall, the proposed framework supports more transparent and anatomically interpretable AI for fundus-based sex classification.