论文提出OrthKD蒸馏方法，用异构教师知识教轻量模型，DR筛查准确率从0.507涨到0.728，适合边缘部署。
OrthKD提出一种选择性信任知识蒸馏框架，针对强CNN教师（EfficientNet-B3，0.876 QWK）与弱Transformer教师（Swin-Base，0.830 QWK）的互补性，仅从强教师转移完整监督，从弱教师提取特征，并强制学生投影正交化。在132049张视网膜图像上，5.4M参数的MobileNetV3学生模型在EyePACS上达到0.885 QWK，零样本Messidor-2性能从0.507提升至0.728 QWK，同时具备强转诊AUC和校准能力。该方法在资源受限设备上实现了实用的DR筛查。
OrthKD: Extracting Generalized Clinical Knowledge from Heterogeneous Teachers for Lightweight Deployment
Deploying diabetic retinopathy (DR) screening models in primary care requires edge-efficient systems that remain accurate, safe, and reliable under domain shift. Multi-teacher knowledge distillation (KD) is a natural compression strategy, but existing approaches largely assume that all teachers provide equally trustworthy supervision. In our setting, this assumption fails: a strong CNN teacher (EfficientNet-B3, 0.876 QWK) and a weaker Transformer teacher (Swin-Base, 0.830 QWK) are complementary, yet the Transformer's logits can still mislead the student. We therefore propose OrthKD, a selective-trust distillation framework that transfers full supervision from the strong CNN, uses feature-only distillation from the weak ViT, and enforces orthogonality between teacher-specific student projections to encourage complementary rather than redundant evidence. This design preserves local lesion precision, injects global structural context, and improves robustness to distribution shift. On 132,049 retinal images, a 5.4M-parameter MobileNetV3 student reaches 0.885 QWK on EyePACS and improves zero-shot Messidor-2 performance from 0.507 to 0.728 QWK, while also achieving strong referral AUC and calibration. These results show that selectively distilling heterogeneous teachers can enable practical DR screening on resource-constrained devices.