A Generative Framework for Bidirectional Image-Report Understanding in Chest Radiography

• URL: http://arxiv.org/abs/2502.05926v1
• Date: Sun, 09 Feb 2025 15:02:57 GMT
• Title: A Generative Framework for Bidirectional Image-Report Understanding in Chest Radiography
• Authors: Nicholas Evans, Stephen Baker, Miles Reed,
• Abstract summary: Multi-Stage Adaptive Vision-Language Tuning (MAViLT) is a novel framework designed to enhance multimodal reasoning and generation for vision-based understanding. MAViLT incorporates a clinical gradient-weighted tokenization process and a hierarchical fine-tuning strategy, enabling it to generate accurate radiology reports, synthesize realistic CXRs from text, and answer vision-based clinical questions. We evaluate MAViLT on two benchmark datasets, MIMIC-CXR and Indiana University CXR, achieving state-of-the-art results across all tasks.
• Score: 1.2289361708127877
• License:
• Abstract: The rapid advancements in large language models (LLMs) have unlocked their potential for multimodal tasks, where text and visual data are processed jointly. However, applying LLMs to medical imaging, particularly for chest X-rays (CXR), poses significant challenges due to the need for precise visual-textual alignment and the preservation of critical diagnostic details. In this paper, we propose Multi-Stage Adaptive Vision-Language Tuning (MAViLT), a novel framework designed to enhance multimodal reasoning and generation for CXR understanding. MAViLT incorporates a clinical gradient-weighted tokenization process and a hierarchical fine-tuning strategy, enabling it to generate accurate radiology reports, synthesize realistic CXRs from text, and answer vision-based clinical questions. We evaluate MAViLT on two benchmark datasets, MIMIC-CXR and Indiana University CXR, achieving state-of-the-art results across all tasks. Human evaluations further validate the clinical relevance and utility of MAViLT, making it a robust tool for real-world medical applications. This work demonstrates the feasibility of leveraging LLMs for multimodal medical imaging while addressing key challenges in vision-language integration.

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