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Direct visual adaptation in few-shot medical image segmentation outperforms prompt-based strategies, revealing critical insights into model performance.
You can train a surprisingly effective medical imaging MLLM using *only* public data by generating knowledge-aware reasoning traces and enforcing self-consistency during reinforcement learning.
