Kvasir-VQA: A Text-Image Pair GI Tract Dataset

Sushant Gautam, Andrea Storås, Cise Midoglu, Steven Hicks, Vajira Thambawita, Pål Halvorsen, Michael Riegler

Kvasir-VQA: A Text-Image Pair GI Tract Dataset: 71 upvotes on Hugging Face Daily Papers, #1 of 19 papers on 2024-09-04. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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