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Fine-tuning a general grounding model on a newly constructed dataset boosts medical localization accuracy from 10.64 to 85.59, showcasing the power of domain-specific supervision.
Echo-DM achieves superior ultrasound marker removal while maintaining anatomical integrity, challenging the effectiveness of existing marker removal techniques.
By unifying specialized detectors with MLLMs in an agentic framework, Echo-{\alpha} achieves state-of-the-art ultrasound interpretation, suggesting a path to more accurate, interpretable, and transferable medical AI.
