In recent years, large-scale artificial general intelligence models have achieved unprecedented success in various general-domain tasks. However, their direct application to specialized fields like medical imaging remains challenging due to the field’s inherent complexities. Unlike natural images, which feature everyday objects recognizable through common sense, medical images demand deep domain expertise for accurate interpretation. This makes the annotation process particularly challenging, as producing a large volume of high-quality labeled medical data requires the involvement of multiple experts, a resource that is often limited and difficult to scale. Moreover, while some medical imaging modalities, such as color fundus photography, benefit from extensive annotated datasets, others, like Fluorescein Angiography (FA), suffer from a severe lack of labeled data due to their specialized and less frequently used nature.