Update README.md

README.md

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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.
 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](https://huggingface.co/datasets/BearSubj13/CoronaryDominance/blob/main/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
 
-<img src="https://huggingface.co/datasets/BearSubj13/CoronaryDominance/blob/main/dataset_scheme.png" alt="Dataset scheme" width="700"/>
 
-![Dataset scheme](https://huggingface.co/datasets/BearSubj13/CoronaryDominance/blob/main/dataset_scheme.png "Dataset scheme")
+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}, 
+}