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--- |
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annotations_creators: [] |
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language: en |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- object-detection |
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task_ids: [] |
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pretty_name: arcade_combined_export |
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tags: |
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- fiftyone |
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- image |
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- object-detection |
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dataset_summary: ' |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 samples. |
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## Installation |
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If you haven''t already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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from fiftyone.utils.huggingface import load_from_hub |
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# Load the dataset |
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# Note: other available arguments include ''max_samples'', etc |
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dataset = load_from_hub("pjramg/arcade_fiftyone") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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' |
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--- |
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# Dataset Card for arcade_combined_export |
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<!-- Provide a quick summary of the dataset. --> |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 samples. |
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## Installation |
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If you haven't already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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from fiftyone.utils.huggingface import load_from_hub |
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# Load the dataset |
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# Note: other available arguments include 'max_samples', etc |
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dataset = load_from_hub("pjramg/arcade_fiftyone") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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# ARCADE Combined Dataset (FiftyOne Format) |
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The **ARCADE Combined Dataset** is a curated collection of coronary angiography images and annotations designed to evaluate coronary artery stenosis. This version has been processed and exported using [FiftyOne](https://voxel51.com/fiftyone), and includes cleaned segmentation data, metadata fields for clinical context, and embedded visual labels. |
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## Dataset Structure |
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- `segmentations`: COCO-style detection masks per coronary artery segment. |
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- `phase`: The acquisition phase of the angiography video. |
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- `task`: A specific labeling task (segmentation or regression) is used. |
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- `subset_name`: Subdivision info (train, val, test). |
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- `coco_id`: Corresponding COCO ID for alignment with original sources. |
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- `filepath`: Path to the image file. |
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- `metadata`: Image metadata including dimensions and pixel spacing. |
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## Format |
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This dataset is stored in **FiftyOneDataset format**, which consists of: |
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- `data.json`: Metadata and label references |
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- `data/`: Folder containing all image samples |
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- Optional: auxiliary files (e.g., `README.md`, config, JSON index) |
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To load it in Python: |
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```python |
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import fiftyone as fo |
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dataset = fo.Dataset.from_dir( |
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dataset_dir="arcade_combined_fiftyone", |
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dataset_type=fo.types.FiftyOneDataset, |
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) |
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``` |
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## Source |
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The original ARCADE dataset was introduced in the paper: |
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Labrecque Langlais et al. (2023) — Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies. |
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https://doi.org/10.21203/rs.3.rs-3610879/v1 |
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This combined version aggregates and restructures subsets across tasks and phases, harmonized with FiftyOne tooling for streamlined model training and evaluation. |
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## License |
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This dataset is shared for research and academic use only. Please consult the original dataset license for clinical or commercial applications. |
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## Citation |
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```bibtex |
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@article{avram2023evaluation, |
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title={Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies}, |
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author={Labrecque Langlais, E. and Corbin, D. and Tastet, O. and Hayek, A. and Doolub, G. and Mrad, S. and Tardif, J.-C. and Tanguay, J.-F. and Marquis-Gravel, G. and Tison, G. and Kadoury, S. and Le, W. and Gallo, R. and Lesage, F. and Avram, R.}, |
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year={2023} |
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} |
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``` |
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## Dataset Card Contact |
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[Paula Ramos](https://huggingface.co/datasets/pjramg) |
