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Synthetic data alone can achieve 87% of real-data performance for cerebral microbleed detection, but architecture choice critically determines transferability.
CARDEA is a unified large vision-language model that serves as the inference core of a CAG pipeline that runs an end-to-end CAG pipeline from raw multi-view videos through keyframe selection to study-level diagnosis while exposing auditable spatial evidence behind its conclusions.