Libra: Leveraging Temporal Images for Biomedical Radiology Analysis

• URL: http://arxiv.org/abs/2411.19378v2
• Date: Sun, 16 Feb 2025 17:29:23 GMT
• Title: Libra: Leveraging Temporal Images for Biomedical Radiology Analysis
• Authors: Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho,
• Abstract summary: Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. 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.
• Score: 21.772106685777995
• License:
• Abstract: 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.

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