Core Concepts
Proposing Dia-LLaMA framework for CT report generation using LLM with diagnostic prompts.
Abstract
The content introduces Dia-LLaMA, a framework for generating CT reports using large language models (LLMs). It addresses challenges in medical report generation, focusing on the imbalance between normal and abnormal cases. The paper proposes leveraging diagnostic information to guide LLMs in generating more accurate and reliable reports. Experiments show that Dia-LLaMA outperforms existing methods in clinical efficacy and natural language generation metrics.
Structure:
Introduction to Medical Report Generation Challenges
Proposed Dia-LLaMA Framework Overview
Methodology: Disease-Aware Attention, Disease Prototype Memory Bank, Diagnostic Text Prompts
Experiments and Results: Dataset, Metrics, Implementation Details, Comparison with SOTA Methods, Ablation Study
Conclusion and Future Work
Stats
"Experiments on the chest CT dataset demonstrated that our proposed method outperformed previous methods."
"Our method achieved state-of-the-art on both clinical efficacy performance and natural language generation metrics."