R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation

Researchers introduced R2GenCSR, a new framework for generating radiology reports using large language models (LLMs), which addresses the challenge of extracting effective information and reducing computational complexity by employing Mamba as a vision backbone and context retrieval techniques. This approach enhances feature representation and discriminative learning, leading to high-quality medical report generation with comparable performance to strong Transformer models but at lower computational cost.

Read the full article at arXiv cs.CL (NLP)

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