ArticleScientific data2020
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 2 of them syntheses that pooled it.
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Who cites it
137 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Pooled it
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Lumina-Net: temporal learning with feature fusion for endoscopic artifact removal.Medical & biological engineering & computing · 2026Article
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Uncertainty-Aware Prediction Across Endoscopic Domains: Laryngeal Narrow-Band and Gastrointestinal Imaging.Biomedicines · 2026Article
- Evaluating ChatGPT-5 for Detection of Barrett's Esophagus and Grading of Esophagitis: A Multiclass Endoscopic Image Analysis.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Self-supervised monocular depth estimation for colonoscopy using normal-guided cross-attention.International journal of computer assisted radiology and surgery · 2026Article
- GeriAIGastroNet: AI-Assisted Gastrointestinal Polyp Segmentation and Severity-Based Triage for Tele-Gastroenterology in Underserved Geriatric Populations.Journal of clinical medicine · 2026Article
- A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.Scientific data · 2026Article
- Multi-modal monocular endoscopic depth and pose estimation with edge-guided self-supervision.International journal of computer assisted radiology and surgery · 2026Article
- Evaluating StyleGAN2-ADA and f-AnoGAN for GAN based unsupervised anomaly detection in gastrointestinal endoscopy.Scientific reports · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- GAD-YOLO: a gastrointestinal abnormality detection YOLO model with multi-scale channel attention and residual fusion.Medical & biological engineering & computing · 2026Article
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Gastroenterological disease detection using transformer-based medical imaging for sustainable healthcare.Scientific reports · 2026Article
- Federated Gastrointestinal Lesion Classification with Clinical-Entropy Guided Quantum-Inspired Token Pruning in Vision Transformers.Diagnostics (Basel, Switzerland) · 2026Article
- InfoColon: A dataset for consecutive informative frames in Colonoscopy.Scientific data · 2026Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.