Kvasir-VQA: A Text-Image Pair GI Tract Dataset
Authors: name: Sushant Gautam, name: Andrea Storås, name: Cise Midoglu, name: Steven A. Hicks, name: Vajira Thambawita, name: Pål Halvorsen, name: Michael A. Riegler

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.