---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- object-detection
task_ids: []
pretty_name: deeplesion_balanced
tags:
- fiftyone
- image
- object-detection
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2161 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/deeplesion_balanced_fiftyone")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# DeepLesion Benchmark Subset (Balanced 2K)

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.

## Dataset Details

- **Source**: [DeepLesion](https://nihcc.app.box.com/v/DeepLesion)
- **Institution**: National Institutes of Health (NIH) Clinical Center
- **Subset size**: 2,000 images
- **Lesion types**: lung, abdomen, mediastinum, liver, pelvis, soft tissue, kidney, bone
- **Selection criteria**:
  - Short diameter > 10mm
  - Balanced sampling across all types
- **Windowing**: All slices were windowed using DICOM parameters and converted to 8-bit PNG format

## License

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.

> This dataset is intended **only for non-commercial research and educational use**.  
> You must credit the original authors and the NIH Clinical Center when using this data.

## Citation

If you use this data, please cite:

```bibtex
@article{yan2018deeplesion,
title={DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning},
author={Yan, Ke and Zhang, Yao and Wang, Le Lu and Huang, Xuejun and Summers, Ronald M},
journal={Journal of medical imaging},
volume={5},
number={3},
pages={036501},
year={2018},
publisher={SPIE}
}

Curation done by FiftyOne.
@article{moore2020fiftyone,
  title={FiftyOne},
  author={Moore, B. E. and Corso, J. J.},
  journal={GitHub. Note: https://github.com/voxel51/fiftyone},
  year={2020}
}
```
## Intended Uses

- Embedding demos
- Lesion similarity and retrieval
- Benchmarking medical image models
- Few-shot learning on lesion types

## Limitations

- This is a small subset of the full DeepLesion dataset
- Not suitable for training full detection models
- Labels are coarse and may contain inconsistencies

## Contact

Created by Paula Ramos for demo purposes using FiftyOne and the DeepLesion public metadata.