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<p>Diabetic Retinopathy (DR), a leading cause of preventable blindness worldwide, underscores the urgent need for robust AI-driven diagnostic tools. Although various deep learning models for retinal imaging have emerged, their evaluation remains constrained by limited public available datasets that lack both large-scale coverage and fine-grained annotations, compromising reliable assessments of model generalizability. To bridge this gap, we introduce a comprehensive multimodal dataset that includes three key retinal imaging modalities: color fundus photography (CFP), optical coherence tomography (OCT), and ultrawide-field fundus imaging (UWF). Our dataset is unprecedented in scale and modality diversity, provides detailed lesion-level annotations and severity grades for DR and Diabetic Macular Edema (DME). We benchmark a range of fundus foundation models and large vision-language models on this dataset, revealing critical performance gaps and domain-specific challenges. By unifying large-scale multimodal data with precisely annotated clinical labels, our work establishes a foundational benchmark to drive advances in AI reliability and real-world clinical utility.</p>

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A multimodal retinal image dataset for diabetic retinopathy detection using foundation models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Zhenyu Tang,
  • Lilong Wang,
  • Zhen Guo,
  • Qianqian Liang,
  • Shuyue Xue,
  • Chengcheng Feng,
  • Lili Ran,
  • Lingzhi Chen,
  • Xiaosong Wang,
  • Jun Li

摘要

Diabetic Retinopathy (DR), a leading cause of preventable blindness worldwide, underscores the urgent need for robust AI-driven diagnostic tools. Although various deep learning models for retinal imaging have emerged, their evaluation remains constrained by limited public available datasets that lack both large-scale coverage and fine-grained annotations, compromising reliable assessments of model generalizability. To bridge this gap, we introduce a comprehensive multimodal dataset that includes three key retinal imaging modalities: color fundus photography (CFP), optical coherence tomography (OCT), and ultrawide-field fundus imaging (UWF). Our dataset is unprecedented in scale and modality diversity, provides detailed lesion-level annotations and severity grades for DR and Diabetic Macular Edema (DME). We benchmark a range of fundus foundation models and large vision-language models on this dataset, revealing critical performance gaps and domain-specific challenges. By unifying large-scale multimodal data with precisely annotated clinical labels, our work establishes a foundational benchmark to drive advances in AI reliability and real-world clinical utility.