Libra：利用时序图像进行生物医学放射学分析
Libra: Leveraging Temporal Images for Biomedical Radiology Analysis
AI总结：
本文提出Libra，一种时序感知多模态大语言模型，通过放射学图像编码器和时序对齐连接器（TAC）捕获胸部X光片间的时序差异，在MIMIC-CXR数据集上达到最先进水平。
摘要：
放射学报告生成（RRG）需要先进的医学图像分析、有效的时序推理和准确的文本生成。虽然多模态大语言模型（MLLMs）与预训练的视觉编码器对齐以增强视觉-语言理解，但大多数现有方法依赖单图像分析或基于规则的启发式方法来处理多幅图像，未能充分利用多模态医学数据集中的时序信息。在本文中，我们引入了Libra，一种专为胸部X光报告生成而设计的时序感知多模态大语言模型。Libra结合了放射学专用图像编码器与新颖的时序对齐连接器（TAC），旨在准确捕获并整合配对当前图像与先前图像之间的时序差异。在MIMIC-CXR数据集上的大量实验表明，Libra在同等规模的多模态大语言模型中建立了新的最先进基准，在临床相关性和词汇准确性方面均树立了新标准。
英文摘要：
Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. While multimodal large language models (MLLMs) align with pre-trained vision encoders to enhance visual-language understanding, most existing methods rely on single-image analysis or rule-based heuristics to process multiple images, failing to fully leverage temporal information in multi-modal medical datasets. In this paper, we introduce Libra, a temporal-aware MLLM tailored for chest X-ray report generation. Libra combines a radiology-specific image encoder with a novel Temporal Alignment Connector (TAC), designed to accurately capture and integrate temporal differences between paired current and prior images. Extensive experiments on the MIMIC-CXR dataset demonstrate that Libra establishes a new state-of-the-art benchmark among similarly scaled MLLMs, setting new standards in both clinical relevance and lexical accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
