VLM-KG: Multimodal Radiology Knowledge Graph Generation

• URL: http://arxiv.org/abs/2505.17042v1
• Date: Tue, 13 May 2025 06:11:10 GMT
• Title: VLM-KG: Multimodal Radiology Knowledge Graph Generation
• Authors: Abdullah Abdullah, Seong Tae Kim,
• Abstract summary: We propose a novel framework for knowledge graph generation in radiology.<n>Our approach outperforms previous methods and introduces the first multimodal solution for radiology knowledge graph generation.
• Score: 3.6669792968901573
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated remarkable success in natural language generation, excelling at instruction following and structured output generation. Knowledge graphs play a crucial role in radiology, serving as valuable sources of factual information and enhancing various downstream tasks. However, generating radiology-specific knowledge graphs presents significant challenges due to the specialized language of radiology reports and the limited availability of domain-specific data. Existing solutions are predominantly unimodal, meaning they generate knowledge graphs only from radiology reports while excluding radiographic images. Additionally, they struggle with long-form radiology data due to limited context length. To address these limitations, we propose a novel multimodal VLM-based framework for knowledge graph generation in radiology. Our approach outperforms previous methods and introduces the first multimodal solution for radiology knowledge graph generation.

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