Decentralized LoRA Augmented Transformer with Context-aware Multi-scale Feature Learning for Secured Eye Diagnosis
This work addresses data imbalance, privacy, and interpretability problems for medical imaging in eye diagnosis, though it appears incremental as it combines existing techniques like LoRA and federated learning in a new application.
The paper tackled the challenge of accurate and privacy-preserving ophthalmic disease diagnosis by proposing a novel transformer-based framework that integrates multi-scale feature learning, LoRA, knowledge distillation, and federated learning, achieving consistent outperformance over traditional CNNs and state-of-the-art transformers on benchmark datasets like OCTDL and Eye Disease Image Dataset in metrics such as AUC, F1 score, and precision.
Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations of existing deep learning models in handling data imbalance, data privacy concerns, spatial feature diversity, and clinical interpretability. This paper proposes a novel Data efficient Image Transformer (DeiT) based framework that integrates context aware multiscale patch embedding, Low-Rank Adaptation (LoRA), knowledge distillation, and federated learning to address these challenges in a unified manner. The proposed model effectively captures both local and global retinal features by leveraging multi scale patch representations with local and global attention mechanisms. LoRA integration enhances computational efficiency by reducing the number of trainable parameters, while federated learning ensures secure, decentralized training without compromising data privacy. A knowledge distillation strategy further improves generalization in data scarce settings. Comprehensive evaluations on two benchmark datasets OCTDL and the Eye Disease Image Dataset demonstrate that the proposed framework consistently outperforms both traditional CNNs and state of the art transformer architectures across key metrics including AUC, F1 score, and precision. Furthermore, Grad-CAM++ visualizations provide interpretable insights into model predictions, supporting clinical trust. This work establishes a strong foundation for scalable, secure, and explainable AI applications in ophthalmic diagnostics.