Kvasir-VQA: A Text-Image Pair GI Tract Dataset
This provides a new dataset for researchers in medical image analysis, but it is incremental as it extends existing datasets with annotations.
The authors introduced Kvasir-VQA, a dataset of 6,500 annotated GI tract images with question-answer pairs, to support tasks like VQA and image captioning in medical diagnostics, demonstrating its effectiveness in training models for selected tasks.
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.