April 18, 2024, 4:44 a.m. | Hongzhao Li, Hongyu Wang, Xia Sun, Hua He, Jun Feng

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.11209v1 Announce Type: cross
Abstract: Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based …

abstract arxiv clear cs.ai cs.cv cs.mm current errors generate however images llm medical physicians prompt radiology ray report reports type x-ray

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