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--- |
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license: mit |
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task_categories: |
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- video-classification |
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language: |
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- en |
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tags: |
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- angiography |
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- cardiology |
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- X-ray |
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- multi-view |
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- video |
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- coronary |
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- dominance |
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- medical |
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- imaging |
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- stenosis |
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- occlusion |
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- artery |
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- uncertainty |
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- outliers |
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pretty_name: coronary_dominamnce |
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size_categories: |
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- 10B<n<100B |
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--- |
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The dataset containes invasive coronary angiograms for the coronary dominance classification task, an essential aspect in assessing the severity of coronary artery disease. |
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The dataset holds 1,574 studies, including X-ray multi-view videos from two different interventional angiography systems. |
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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. |
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 |
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More information about coronary dominance classification using neural networks in https://doi.org/10.48550/arXiv.2309.06958. |
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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 |
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CITATION |
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Please cite: |
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``` |
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@misc{ponomarchuk2024endtoendsyntaxscoreprediction, |
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title={End-to-end SYNTAX score prediction: benchmark and methods}, |
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author={Alexander Ponomarchuk and Ivan Kruzhilov and Galina Zubkova and Artem Shadrin and Ruslan Utegenov and Ivan Bessonov and Pavel Blinov}, |
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year={2024}, |
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eprint={2407.19894}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2407.19894}, |
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} |
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``` |
