用超广角眼底影像和深度学习，提升糖尿病视网膜病变检测准确率。
Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema
- 融合频域与空间域特征，结合视觉变压器与基础模型增强分析能力。
- 在UWF4DR数据集上对可转诊糖尿病视网膜病变识别准确率达93.7%。
- 通过梯度加权可视化提升模型可解释性，适合医疗AI研究者参考。
糖尿病视网膜病变（DR）和糖尿病性黄斑水肿（DME）是工作人群可预防性失明的主要原因。传统方法多依赖标准彩色眼底照相（CFP），而近年超广角成像（UWF）提供了比CFP更广阔的视野。为此，本研究探索先进深度学习（DL）方法与UWF影像在三个临床任务中的应用：一）UWF图像质量评估；二）可转诊糖尿病视网膜病变（RDR）识别；三）DME识别。基于MICCAI 2024会议发布的公开数据集UWF4DR Challenge，我们对比了空间（RGB）与频域的DL模型，涵盖主流卷积神经网络（CNN）、近期视觉变压器（ViTs）及基础模型，并引入特征级融合以提升鲁棒性。此外，利用Grad-CAM分析模型决策过程，增强可解释性。结果表明，所提方案在各类架构中均表现优异，凸显了新兴ViTs与基础模型、特征级融合及频域表示在UWF分析中的潜力。
原文摘要 · Abstract (English)
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approaches in the literature focus on standard color fundus photography (CFP) for the detection of these conditions. Nevertheless, recent ultra-widefield imaging (UWF) offers a significantly wider field of view in comparison to CFP. Motivated by this, the present study explores state-of-the-art deep learning (DL) methods and UWF imaging on three clinically relevant tasks: i) image quality assessment for UWF, ii) identification of referable diabetic retinopathy (RDR), and iii) identification of DME. Using the publicly available UWF4DR Challenge dataset, released as part of the MICCAI 2024 conference, we benchmark DL models in the spatial (RGB) and frequency domains, including popular convolutional neural networks (CNNs) as well as recent vision transformers (ViTs) and foundation models. In addition, we explore a final feature-level fusion to increase robustness. Finally, we also analyze the decisions of the DL models using Grad-CAM, increasing the explainability. Our proposal achieves consistently strong performance across all architectures, underscoring the competitiveness of emerging ViTs and foundation models and the promise of feature-level fusion and frequency-domain representations for UWF analysis.
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