Learn to Learn: A Mirror Meta-Learning Method for Retinal Disease Diagnosis on Fundus Images

Retinal diseases, such as glaucoma, age-related macular degeneration, and high myopia, are major contributors to global vision loss, emphasizing the need for early detection and intervention. Current deep learning approaches for diagnosing retinal diseases using fundus images primarily focus on single-disease classification due to the scarcity and expense of diverse datasets. This limitation restricts their generalization across multiple ocular diseases and impedes transfer learning to untrained disease types. In this paper, we introduce a novel Model-Agnostic Meta-Learning framework, called Mirror Meta-Learning (MML) , which incorporates an autoencoder module to supervise the backpropagation path in few-shot learning, enhancing model initialization and adaptation. MML’s effectiveness is validated using four publicly available retinal disease binary classification datasets and a proprietary high myopia dataset. In addition, MML demonstrates robustness when tested on three well-established few-shot learning datasets. Our results show the proposed model’s superiority in terms of performance and generalizability in ocular disease classification tasks.