  Current fundus image analysis models are predominantly built for specific
tasks relying on individual datasets. The learning process is usually based on
data-driven paradigm without prior knowledge, resulting in poor transferability
and generalizability. To address this issue, we propose MM-Retinal, a
multi-modal dataset that encompasses high-quality image-text pairs collected
from professional fundus diagram books. Moreover, enabled by MM-Retinal, we
present a novel Knowledge-enhanced foundational pretraining model which
incorporates Fundus Image-Text expertise, called KeepFIT. It is designed with
image similarity-guided text revision and mixed training strategy to infuse
expert knowledge. Our proposed fundus foundation model achieves
state-of-the-art performance across six unseen downstream tasks and holds
excellent generalization ability in zero-shot and few-shot scenarios.
MM-Retinal and KeepFIT are available at https://github.com/lxirich/MM-Retinal.
