Summary of Kvasir-vqa: a Text-image Pair Gi Tract Dataset, by Sushant Gautam et al.
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
by Sushant Gautam, Andrea Storås, Cise Midoglu, Steven A. Hicks, Vajira Thambawita, Pål Halvorsen, Michael A. Riegler
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Kvasir-VQA, a large dataset for advanced machine learning tasks in Gastrointestinal (GI) diagnostics. The dataset combines 6,500 images of GI tract conditions and surgical instruments with question-and-answer annotations, supporting various question types. It aims to facilitate applications such as image captioning, Visual Question Answering (VQA), synthetic medical image generation, object detection, and classification. Experimental results demonstrate the effectiveness of Kvasir-VQA in training models for three selected tasks, showcasing significant potential in medical image analysis and diagnostics. Evaluation metrics are presented for each task, highlighting the dataset’s usability and versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big tool to help computers understand medical images better. They made a huge collection of pictures of different parts of the stomach and surgical instruments, with questions about what they show. This can help computers do tasks like describing images, answering questions about them, making fake images that look real, finding objects in images, and classifying things into categories. The researchers tested their tool and showed it works well for three important medical image analysis tasks. |
Keywords
» Artificial intelligence » Classification » Image captioning » Image generation » Machine learning » Object detection » Question answering
