Libra: Leveraging Temporal Images for Biomedical Radiology Analysis

Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho

Keywords: Radiology Report Generation, Chest X-rays, Visual Language Model, Large Language Models, Image-Text Alignment, Medical AI

TL;DR: Our paper presents Libra, a novel vision-language model that incorporates temporal changes in chest X-rays to generate accurate and context-aware radiology reports, substantially improving clinical and lexical performance metrics.

Abstract: Radiology report generation (RRG) is a challenging task, as it requires a thorough understanding of medical images, integration of multiple temporal inputs, and accurate report generation. Effective interpretation of medical images, such as chest X-rays (CXRs), demands sophisticated visual-language reasoning to map visual findings to structured reports. Recent studies have shown that multimodal large language models (MLLMs) can acquire multimodal capabilities by aligning with pre-trained vision encoders. However, current approaches predominantly focus on single-image analysis or utilise rule-based symbolic processing to handle multiple images, thereby overlooking the essential temporal information derived from comparing current images with prior ones. To overcome this critical limitation, we introduce Libra, a temporal-aware MLLM tailored for CXR report generation using temporal images. Libra integrates a radiology-specific image encoder with a MLLM and utilises a novel Temporal Alignment Connector to capture and synthesise temporal information of images across different time points with unprecedented precision. Extensive experiments show that Libra achieves new state-of-the-art performance among the same parameter scale MLLMs for RRG tasks on the MIMIC-CXR. Specifically, Libra improves the RadCliQ metric by 12.9\% and makes substantial gains across all lexical metrics compared to previous models.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 11540