April 22, 2024, 4:45 a.m. | Tiancheng Gu, Kaicheng Yang, Dongnan Liu, Weidong Cai

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.13039v1 Announce Type: new
Abstract: Medical visual question answering (Med-VQA) aims to automate the prediction of correct answers for medical images and questions, thereby assisting physicians in reducing repetitive tasks and alleviating their workload. Existing approaches primarily focus on pre-training models using additional and comprehensive datasets, followed by fine-tuning to enhance performance in downstream tasks. However, there is also significant value in exploring existing models to extract clinically relevant information. In this paper, we propose the Latent Prompt Assist model …

arxiv cs.cl cs.cv medical prompt question question answering type visual

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