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Kvasir-SEG: Gastrointestinal Polyp Segmentation Dataset
Dataset Description
Kvasir-SEG is a gastrointestinal polyp segmentation dataset containing 1000 polyp images with corresponding segmentation masks from the Kvasir dataset.
- Task: Binary segmentation (polyp vs. background)
- Modality: Gastrointestinal endoscopy
- Format: PNG images (332x487 to 1920x1072 pixels) with binary masks
- Splits: Training, Validation, and Test sets
Dataset Structure
kvasir-seg/
├── train/
│ ├── images/ # Training images
│ └── masks/ # Training masks
├── validation/
│ ├── images/ # Validation images
│ └── masks/ # Validation masks
└── test/
├── images/ # Test images
└── masks/ # Test masks
Citation
If you use this dataset, please cite:
@inproceedings{jha2020kvasir,
title={Kvasir-SEG: A Segmented Polyp Dataset},
author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{a}l and de Lange, Thomas and Johansen, Dag and Johansen, H{a}vard D},
booktitle={International Conference on Multimedia Modeling},
pages={451--462},
year={2020},
organization={Springer}
}
Dataset Split: This train/validation/test split is created by the following study. Please find more details in:
@article{chang2024esfpnet,
title={ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in Endoscopic Video},
author={Chang, Qi and Ahmad, Danish and Toth, Jennifer and Bascom, Rebecca and Higgins, William E},
journal={Journal of Imaging},
volume={10},
number={8},
pages={191},
year={2024},
publisher={MDPI}
}
Usage
from datasets import load_dataset
# Load dataset
dataset = load_dataset("Angelou0516/kvasir-seg")
# Access splits
train_data = dataset['train']
val_data = dataset['validation']
test_data = dataset['test']
# Access a sample
sample = train_data[0]
image = sample['file_name'] # Image path
label = sample['label'] # Segmentation mask path
License
Please refer to the original Kvasir dataset license and citation requirements.
Links
- Original Dataset: https://datasets.simula.no/kvasir-seg/
- Paper: https://link.springer.com/chapter/10.1007/978-3-030-37734-2_37
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