A Meta-Learning Network Guided by Domain Knowledge of Fundus Images for the Diagnosis of High Myopia

The diagnosis of high myopia using fundus images is essential for visual health. Existing deep learning-based methods rely on large-scale labeled data, but annotated data for high myopia fundus images remains scarce. To address this issue, we mimic the ability of ophthalmologists to diagnose a new disease with only a small number of samples. In this regard, we propose a meta-learning network guided by domain knowledge from fundus images for diagnosing high myopia. The model consists of three modules: First, the image reconstruction module builds an autoencoder (comprising an encoder and a decoder) that takes the input raw image and outputs the reconstructed image. Next, the fundus image domain knowledge learning module constructs a Siamese network to learn the similarity between the original and reconstructed fundus images. This similarity is used as a loss function to guide the encoder in effectively learning the domain features of fundus images. Finally, in the domain-knowledge-guided meta-learning module, the encoder’s initialization parameters (obtained from the first two modules) are further optimized using the OCMAMAL architecture, resulting in more optimal encoder parameters. This optimization helps achieve superior recognition performance with only a small amount of data for new tasks. Using a clinically real high myopia fundus image dataset, our method achieved F1 scores of 83.4%, 87.9%, and 89.2% under 5-shot, 10-shot, and 20-shot conditions, respectively, demonstrating the effectiveness of the proposed method.