title: Kvasir-VQA: A Text-Image Pair GI Tract Dataset
summary: 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_authors._id: 66d8042419214d743c1687f0
paper_authors.name: Sushant Gautam
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T07:14:53.784Z
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paper_authors.fullname: Sushant Gautam
paper_authors.type: user
paper_authors.user: SushantGautam
paper_authors._id: 66d8042419214d743c1687f1
paper_authors.name: Andrea Storås
paper_authors._id: 66d8042419214d743c1687f2
paper_authors.name: Cise Midoglu
paper_authors.status: admin_assigned
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paper_authors.fullname: Cise Midoglu
paper_authors.type: user
paper_authors.user: cise-midoglu
paper_authors._id: 66d8042419214d743c1687f3
paper_authors.name: Steven A. Hicks
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-04T09:43:25.234Z
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paper_authors.fullname: Steven Hicks
paper_authors.type: user
paper_authors.user: stevenah
paper_authors._id: 66d8042419214d743c1687f4
paper_authors.name: Vajira Thambawita
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-04T09:43:31.957Z
paper_authors._id: 6364e9ffdaa65e06f9086fd8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672824650566-6364e9ffdaa65e06f9086fd8.png
paper_authors.fullname: Vajira Thambawita
paper_authors.type: user
paper_authors.user: vlbthambawita
paper_authors._id: 66d8042419214d743c1687f5
paper_authors.name: Pål Halvorsen
paper_authors._id: 66d8042419214d743c1687f6
paper_authors.name: Michael A. Riegler
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T20:39:43.329Z
paper_authors._id: 63b537ee889aa6707f07fa7a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672825823795-63b537ee889aa6707f07fa7a.png
paper_authors.fullname: Michael Riegler
paper_authors.type: user
paper_authors.user: kelkalot
paper_title: Kvasir-VQA: A Text-Image Pair GI Tract Dataset
paper_summary: 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_ai_summary: Kvasir-VQA is a dataset with question-and-answer annotations for GI diagnostics, supporting image captioning, VQA, synthetic image generation, object detection, and classification.
paper_ai_keywords: Visual Question Answering (VQA) image captioning object detection classification