---
license: mit
task_categories:
- video-classification
language:
- en
tags:
- angiography
- cardiology
- X-ray
- multi-view
- video
- coronary
- dominance
- medical
- imaging
- stenosis
- occlusion
- artery
- uncertainty
- outliers
pretty_name: coronary_dominamnce
size_categories:
- 10B<n<100B
---
The dataset containes invasive coronary angiograms for the coronary dominance classification task, an essential aspect in assessing the severity of coronary artery disease.
The dataset holds 1,574 studies, including X-ray multi-view videos from two different interventional angiography systems.
Each study has the following tags: bad quality, artifact, high uncertainty, and occlusion. Those tags help to classify dominance classification more accurately and allow to utilize the dataset for uncertainty estimation and outlier detection.
![Dataset scheme](dataset_scheme.png "Dataset scheme")

More information about coronary dominance classification using neural networks in https://doi.org/10.48550/arXiv.2309.06958.

Some angiographic studies from the dataset are from CardioSYNTAX dataset of coronary agiograms for the SYNTAX score prediction in https://doi.org/10.48550/arXiv.2407.19894


CITATION
Please cite:
```
@misc{ponomarchuk2024endtoendsyntaxscoreprediction,
      title={End-to-end SYNTAX score prediction: benchmark and methods}, 
      author={Alexander Ponomarchuk and Ivan Kruzhilov and Galina Zubkova and Artem Shadrin and Ruslan Utegenov and Ivan Bessonov and Pavel Blinov},
      year={2024},
      eprint={2407.19894},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.19894}, 
}
```