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