通过动态融合全局与局部特征，提升眼底图像对青光眼的筛查准确率。
Enhancing Fundus Image-based Glaucoma Screening via Dynamic Global-Local Feature Integration
- 设计自适应注意力窗口，自动优化关键区域边界以提取特征。
- 引入多头注意力机制，有效融合全局与局部特征，提升判别力。
- 适合在不同设备、人种间图像差异大的真实医疗场景使用。
随着医学人工智能的发展，眼底图像分类器正越来越多地用于眼科诊断。尽管现有分类模型在特定眼底数据集上已取得高准确率，但在真实世界中仍面临挑战：不同成像设备导致的图像质量差异、跨种族训练与测试图像间的不一致，以及青光眼病例边界模糊等问题。本研究通过强调整合全面的眼底图像信息（包括视盘和视杯区域及其他关键图像块）的重要性来应对上述挑战。具体而言，提出一种自适应注意力窗口，可自主确定最佳边界以增强特征提取；同时引入多头注意力机制，通过特征线性读出有效融合全局与局部特征，提升模型的判别能力。实验结果表明，该方法在青光眼分类任务中实现了更优的准确率与鲁棒性。
原文摘要 · Abstract (English)
With the advancements in medical artificial intelligence (AI), fundus image classifiers are increasingly being applied to assist in ophthalmic diagnosis. While existing classification models have achieved high accuracy on specific fundus datasets, they struggle to address real-world challenges such as variations in image quality across different imaging devices, discrepancies between training and testing images across different racial groups, and the uncertain boundaries due to the characteristics of glaucomatous cases. In this study, we aim to address the above challenges posed by image variations by highlighting the importance of incorporating comprehensive fundus image information, including the optic cup (OC) and optic disc (OD) regions, and other key image patches. Specifically, we propose a self-adaptive attention window that autonomously determines optimal boundaries for enhanced feature extraction. Additionally, we introduce a multi-head attention mechanism to effectively fuse global and local features via feature linear readout, improving the model's discriminative capability. Experimental results demonstrate that our method achieves superior accuracy and robustness in glaucoma classification.
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