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University of South Dakota
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Lightweight models can achieve superior polyp segmentation performance by effectively harnessing foundation model induction with minimal computational overhead.
Freezing large LLMs while using a lightweight diagnostic-prior framework can significantly enhance performance in 3D CT report generation with minimal training overhead.
Swap out quadratic cross-attention for linear-time inference in vision-language models without sacrificing fine-grained understanding.