Libra: Leveraging Temporal Images for Biomedical Radiology Analysis
Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho · 2025
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 singleimage 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 radiologyspecific 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.