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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: deeplesion_balanced |
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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 2161 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/deeplesion_balanced_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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# DeepLesion Benchmark Subset (Balanced 2K) |
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This dataset is a curated subset of the [DeepLesion dataset](https://nihcc.app.box.com/v/DeepLesion), prepared for demonstration and benchmarking purposes. It consists of 2,000 CT lesion samples, balanced across 8 coarse lesion types, and filtered to include lesions with a short diameter > 10mm. |
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## Dataset Details |
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- **Source**: [DeepLesion](https://nihcc.app.box.com/v/DeepLesion) |
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- **Institution**: National Institutes of Health (NIH) Clinical Center |
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- **Subset size**: 2,000 images |
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- **Lesion types**: lung, abdomen, mediastinum, liver, pelvis, soft tissue, kidney, bone |
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- **Selection criteria**: |
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- Short diameter > 10mm |
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- Balanced sampling across all types |
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- **Windowing**: All slices were windowed using DICOM parameters and converted to 8-bit PNG format |
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## License |
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This dataset is shared under the **[CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)**, as specified by the NIH DeepLesion dataset creators. |
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> This dataset is intended **only for non-commercial research and educational use**. |
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> You must credit the original authors and the NIH Clinical Center when using this data. |
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## Citation |
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If you use this data, please cite: |
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```bibtex |
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@article{yan2018deeplesion, |
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title={DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning}, |
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author={Yan, Ke and Zhang, Yao and Wang, Le Lu and Huang, Xuejun and Summers, Ronald M}, |
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journal={Journal of medical imaging}, |
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volume={5}, |
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number={3}, |
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pages={036501}, |
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year={2018}, |
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publisher={SPIE} |
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} |
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Curation done by FiftyOne. |
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@article{moore2020fiftyone, |
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title={FiftyOne}, |
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author={Moore, B. E. and Corso, J. J.}, |
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journal={GitHub. Note: https://github.com/voxel51/fiftyone}, |
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year={2020} |
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} |
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``` |
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## Intended Uses |
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- Embedding demos |
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- Lesion similarity and retrieval |
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- Benchmarking medical image models |
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- Few-shot learning on lesion types |
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## Limitations |
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- This is a small subset of the full DeepLesion dataset |
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- Not suitable for training full detection models |
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- Labels are coarse and may contain inconsistencies |
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## Contact |
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Created by Paula Ramos for demo purposes using FiftyOne and the DeepLesion public metadata. |
