MedRG: Medical Report Grounding with Multi-modal Large Language Model
<|reference_start|>MedRG: Medical Report Grounding with Multi-modal Large Language Model: Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis. However, prevailing visual grounding approaches necessitate the manual extraction of key phrases from medical reports, imposing substantial burdens on both system efficiency and physicians. In this paper, we introduce a novel framework, Medical Report Grounding (MedRG), an end-to-end solution for utilizing a multi-modal Large Language Model to predict key phrase by incorporating a unique token, BOX, into the vocabulary to serve as an embedding for unlocking detection capabilities. Subsequently, the vision encoder-decoder jointly decodes the hidden embedding and the input medical image, generating the corresponding grounding box. The experimental results validate the effectiveness of MedRG, surpassing the performance of the existing state-of-the-art medical phrase grounding methods. This study represents a pioneering exploration of the medical report grounding task, marking the first-ever endeavor in this domain.<|reference_end|>
@article{zou2024medrg:,
    title={MedRG: Medical Report Grounding with Multi-modal Large Language Model},
    author={Ke Zou and Yang Bai and Zhihao Chen and Yang Zhou and Yidi Chen and
  Kai Ren and Meng Wang and Xuedong Yuan and Xiaojing Shen and Huazhu Fu},
    journal={arXiv preprint arXiv:2404.06798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06798},
    primaryClass={cs.CV}
}