用深度学习自动分析眼底图像，提升糖尿病视网膜病变检测效率
Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging
- 基于EfficientNet和ResNet的卷积网络处理超广角眼底图
- 在三个任务中均表现稳健，助力早期病变识别
- 适合眼科医生和医疗AI开发者参考
糖尿病视网膜病变和黄斑水肿是糖尿病的重要并发症，可导致视力丧失。通过超广角眼底成像实现早期检测能改善患者预后，但面临图像质量参差和分析规模大的挑战。本文针对MICCAI 2024 UWF4DR挑战赛，提出深度学习方案用于自动化超广角眼底图像分析，涵盖图像质量评估、可转诊性糖尿病视网膜病变检测及黄斑水肿识别三项任务。采用EfficientNet、ResNet等先进卷积神经网络架构，结合预处理与数据增强策略，模型在各项任务中均表现出色。结果表明，深度学习可显著提升超广角眼底图像的自动化分析能力，有望提高临床环境中糖尿病视网膜病变和黄斑水肿检测的效率与准确性。
原文摘要 · Abstract (English)
Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhances patient outcomes but presents challenges in image quality and analysis scale. This paper introduces deep learning solutions for automated UWF image analysis within the framework of the MICCAI 2024 UWF4DR challenge. We detail methods and results across three tasks: image quality assessment, detection of referable DR, and identification of DME. Employing advanced convolutional neural network architectures such as EfficientNet and ResNet, along with preprocessing and augmentation strategies, our models demonstrate robust performance in these tasks. Results indicate that deep learning can significantly aid in the automated analysis of UWF images, potentially improving the efficiency and accuracy of DR and DME detection in clinical settings.
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