Libra: Leveraging Temporal Images for Biomedical Radiology Analysis
This work addresses the challenge of improving radiology report generation for medical applications by better utilizing temporal information, representing an incremental advancement over existing methods.
The paper tackled the problem of generating radiology reports from chest X-ray images by introducing Libra, a temporal-aware multimodal large language model that leverages temporal differences between current and prior images, achieving state-of-the-art performance on the MIMIC-CXR dataset in clinical relevance and lexical accuracy.
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.