Kvasir-VQA：一个文本-图像对胃肠道数据集
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
AI总结：
本研究推出包含6500张标注图像的Kvasir-VQA胃肠道文本-图像对数据集，支持多类问答与多种医学影像任务，实验验证其有效性并提供评估指标，具备高易用性与多功能性。
摘要：
我们推出Kvasir-VQA，这是源自HyperKvasir和Kvasir-Instrument数据集的扩展数据集，新增了问答标注，以支撑胃肠道（GI）诊断领域的高级机器学习任务。该数据集包含6500张标注图像，涵盖多种胃肠道病症和手术器械，支持是/否、选择、位置和数值计数等多种问题类型。数据集可用于图像字幕生成、视觉问答（VQA）、基于文本的合成医学图像生成、目标检测和分类等应用。我们的实验验证了该数据集在训练模型完成三项选定任务上的有效性，展现出其在医学图像分析与诊断中的重要应用价值。我们还为每项任务提供了评估指标，凸显了该数据集的易用性和多功能性。数据集及相关支持资源可在https://datasets.simula.no/kvasir-vqa获取。
英文摘要：
We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
