In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model. Secondly, we establish a scalable pipeline to construct a large-scale medical visual question-answering dataset, named PMC-VQA, which contains 227k VQA pairs of 149k images that cover various modalities or diseases. Thirdly, we pre-train our proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD and SLAKE, outperforming existing work by a large margin. Additionally, we propose a test set that has undergone manual verification, which is significantly more challenging, even the best models struggle to solve.

翻译：本文聚焦于医学视觉问答（MedVQA）问题，该问题对于高效解读含有关键临床信息的医学图像至关重要。首先，我们将MedVQA重新定义为一种自然遵循人机交互模式的生成任务，通过将预训练视觉编码器的视觉信息与大语言模型对齐，提出了一种基于生成的医学视觉理解模型。其次，我们建立了一套可扩展的流水线，构建了名为PMC-VQA的大规模医学视觉问答数据集，该数据集包含149k张图像的227k个VQA对，覆盖多种模态或疾病类型。第三，我们在PMC-VQA上预训练所提模型，随后在多个公开基准（如VQA-RAD和SLAKE）上进行微调，性能大幅超越现有方法。此外，我们提出了一套经过人工验证的测试集，该测试集难度显著提升，即使是顶尖模型也难以攻克。