Kvasir-VQA: A Text-Image Pair GI Tract Dataset
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.
66d8042419214d743c1687f0
Sushant Gautam
claimed_verified
2024-09-04T07:14:53.784Z
62c4779e0a06c039315fb57d
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Sushant Gautam
user
SushantGautam
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Andrea Storås
66d8042419214d743c1687f2
Cise Midoglu
admin_assigned
2024-09-04T09:43:03.417Z
65e0cbf63abb68b76c32f943
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Cise Midoglu
user
cise-midoglu
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Steven A. Hicks
admin_assigned
2024-09-04T09:43:25.234Z
6314cae6b031f7b1c7be31c9
https://cdn-avatars.huggingface.co/v1/production/uploads/6314cae6b031f7b1c7be31c9/x12YwvSn43nZi1-a4ut5v.jpeg
Steven Hicks
user
stevenah
66d8042419214d743c1687f4
Vajira Thambawita
admin_assigned
2024-09-04T09:43:31.957Z
6364e9ffdaa65e06f9086fd8
https://cdn-avatars.huggingface.co/v1/production/uploads/1672824650566-6364e9ffdaa65e06f9086fd8.png
Vajira Thambawita
user
vlbthambawita
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Pål Halvorsen
66d8042419214d743c1687f6
Michael A. Riegler
claimed_verified
2024-11-25T20:39:43.329Z
63b537ee889aa6707f07fa7a
https://cdn-avatars.huggingface.co/v1/production/uploads/1672825823795-63b537ee889aa6707f07fa7a.png
Michael Riegler
user
kelkalot
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
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.
Kvasir-VQA is a dataset with question-and-answer annotations for GI diagnostics, supporting image captioning, VQA, synthetic image generation, object detection, and classification.
Visual Question Answering (VQA) image captioning object detection classification