融合视网膜解剖先验知识，提升眼底图自动青光眼筛查的准确率与可解释性。
Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration
- 设计动态窗口机制，自适应定位诊断关键区域。
- 引入知识增强注意力模块，提升对病灶区域的敏感性，AUC达98.5%。
- 适合临床部署，对罕见病例检测更稳健，适合医学AI研究者参考。
尽管深度学习已推动基于彩色眼底照片的自动化青光眼筛查，但纯数据驱动模型常因受成像伪影干扰而过拟合，并难以捕捉超出预设解剖边界的变化性病理特征。为此，我们提出一种视网膜知识导向框架，将动态多尺度特征学习与领域特定解剖先验相结合。该架构采用三分支结构，联合建模全局视网膜上下文、视盘与视杯结构特征以及动态裁剪的病灶区域。具体地，设计动态窗口机制（DWM），通过图像级监督自适应发现诊断相关区域。同时引入知识增强卷积块注意力模块（KE-CBAM），利用预训练基础模型RETFound中的视网膜先验信息引导空间注意力，防止网络为无关背景噪声分配错误权重。在大规模AIROGS数据集上的实验表明，本方法达到98.5%的AUC和94.6%的准确率。更重要的是，解剖先验的引入有效缓解了类别不平衡问题，显著提升了可转诊青光眼病例的检出能力。在SMDG-19基准上的额外验证进一步证实其优越的跨域泛化能力，表明该工作为真实临床环境下的青光眼诊断提供了鲁棒、可解释且可扩展的解决方案。代码已开源。
原文摘要 · Abstract (English)
While deep learning has advanced automated glaucoma screening via color fundus photography, existing purely data-driven models often overfit to confounding imaging artifacts and struggle to capture unpredictable pathological cues located beyond predefined anatomical boundaries. To address these limitations, we propose a retinal knowledge-oriented framework that synergizes dynamic multi-scale feature learning with domain-specific anatomical priors. The proposed architecture adopts a tri-branch structure to jointly model the global retinal context, the structural characteristics of the optic cup and disc, and dynamically cropped pathological regions. Specifically, we devise a Dynamic Window Mechanism (DWM) that adaptively discovers diagnostically informative patches via image-level supervision. Furthermore, we introduce a Knowledge-Enhanced Convolutional Block Attention Module (KE-CBAM) that explicitly incorporates retinal priors from a pre-trained foundation model RETFound to guide spatial attention, preventing the network from assigning spurious weights to irrelevant background noise. Extensive evaluations on the large-scale AIROGS dataset demonstrate that our method achieves a state-of-the-art AUC of $98.5\%$ and an accuracy of $94.6\%$. More importantly, the integration of anatomical priors effectively mitigates the inherent class imbalance, significantly improving the detection of referable glaucoma cases. Additional validations on the SMDG-19 benchmark further confirm its superior cross-domain generalization, indicating that our contribution provides a robust, interpretable, and scalable solution for real-world clinical glaucoma diagnosis. Our code is available at https://github.com/magiczhuo/Glaucoma-Detection
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