Fusion Information Bottleneck-Driven Vision Transformer Model for Diagnosis of Retinal Diseases via Internet of Medical Things

Ocular diseases are among the leading causes of visual impairment and blindness worldwide, and early, accurate diagnosis is crucial for preventing disease progression. In recent years, deep learning methods based on fundus images have achieved remarkable progress in intelligent diagnosis, yet they still face challenges such as severe overfitting, redundant features, and insufficient capability in fine-grained lesion recognition. These issues become more pronounced in the Internet of Medical Things (IoMT) scenario, further limiting the generalization and practicality of the models. To address this, this paper proposes a Vision Transformer model incorporating Information Bottleneck and Contrastive Learning mechanisms (FIBCL-ViT) for multi-class fundus disease diagnosis. Using ViT as the backbone, the model introduces a variational information bottleneck module to compress redundant features and highlight task-relevant information, while integrating a contrastive learning strategy to enhance feature discriminability, thereby improving the model’s generalization ability and robustness. During training, the model jointly optimizes cross-entropy loss, contrastive loss, and the information bottleneck regularization term to achieve efficient learning. Experimental results demonstrate that the proposed FIBCL-ViT model outperforms mainstream comparison methods across multiple public fundus image datasets, achieving superior performance in terms of accuracy, AUC, and other metrics. For the 7:3 training–testing split, the proposed method achieves an accuracy of 0.9376, a precision of 0.9384, a recall of 0.9374, and an AUC of 0.9902 on public fundus image datasets. This study provides a novel solution for automated screening and remote intelligent diagnosis of ophthalmic diseases, showing strong potential for clinical applications.