The article discusses the development of LLM-CXR, a model focused on improving vision-language alignment in Large Language Models (LLMs) for chest X-ray (CXR) analysis. By leveraging instruction-finetuning, the model aims to enhance its capabilities in understanding and generating visual information from medical images. The approach involves training the model with diverse tasks related to image-based text generation and text-based image generation, leading to improved image-text alignment in both CXR understanding and generation tasks. The method allows the pretrained LLM to gain bidirectional multimodal capabilities without structural modifications or additional networks. Through experiments, it is shown that LLM-CXR outperforms other models specifically designed for subsets of these tasks.
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by Suhyeon Lee,... kl. arxiv.org 03-19-2024
https://arxiv.org/pdf/2305.11490.pdfDybere Forespørgsler