用多数据集训练深度学习模型，实现无创早期青光眼检测
Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images
- 基于EfficientNet-B0架构，跨ACRIMA/ORIGA/RIM-ONE三数据集渐进训练
- 无需复杂预处理即可达到更高AUC-ROC，对未见数据集表现稳健
- 方法可复现且易扩展，适合临床筛查场景
青光眼是导致不可逆失明的主要原因，但早期检测能显著改善治疗效果。传统诊断方法常需侵入性操作和专用设备。本文提出一种基于EfficientNet-B0的深度学习流水线，从视网膜眼底图像中检测青光眼。不同于以往仅依赖单一数据集的研究，我们依次在ACRIMA、ORIGA和RIM-ONE数据集上进行训练与微调，以增强模型泛化能力。实验表明，极简预处理相比复杂增强方法获得更高AUC-ROC值，且模型在未见数据集上仍表现出强区分能力。该方法提供了一种可复现、可扩展的早期青光眼检测方案，具备潜在临床应用价值。
原文摘要 · Abstract (English)
Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes. Traditional diagnostic methods are often invasive and require specialized equipment. In this work, we present a deep learning pipeline using the EfficientNet-B0 architecture for glaucoma detection from retinal fundus images. Unlike prior studies that rely on single datasets, we sequentially train and fine-tune our model across ACRIMA, ORIGA, and RIM-ONE datasets to enhance generalization. Our experiments show that minimal preprocessing yields higher AUC-ROC compared to more complex enhancements, and our model demonstrates strong discriminative performance on unseen datasets. The proposed pipeline offers a reproducible and scalable approach to early glaucoma detection, supporting its potential clinical utility.
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