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 this https URL

提出了 MM-Retinal 基金 us 图像分析模型，该模型通过多模态数据集以及 KeepFIT 知识增强基础预训练模型，实现了六个未见任务中的最先进性能，并在零样本和少样本场景中展现了出色的泛化能力。