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license: apache-2.0
task_categories:
- image-classification
- feature-extraction
tags:
- medical
- pathology
- radiology
- clinical
- oncology
- whole-slide-image
- dicom
- multimodal
size_categories:
- n<1K
pretty_name: HoneyBee Sample Files
---
# HoneyBee Sample Files
Sample data for the [HoneyBee](https://github.com/Lab-Rasool/HoneyBee) framework — a scalable, modular toolkit for multimodal AI in oncology.
These files are used by the HoneyBee example notebooks to demonstrate clinical, pathology, and radiology processing pipelines.
**Paper**: [HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models](https://arxiv.org/abs/2405.07460)
**Package**: [`pip install honeybee-ml`](https://pypi.org/project/honeybee-ml/)
## Files
| File | Type | Size | Description |
|------|------|------|-------------|
| `sample.PDF` | Clinical | 70 KB | De-identified clinical report (PDF) for NLP extraction |
| `sample.svs` | Pathology | 146 MB | Whole-slide image (Aperio SVS) for tissue detection, patch extraction, and embedding |
| `CT/` | Radiology | 105 MB | CT scan with 2 DICOM series (205 slices total) for radiology preprocessing |
### CT Directory Structure
```
CT/
├── 1.3.6.1.4.1.14519.5.2.1.6450.4007.1209.../ (101 slices)
└── 1.3.6.1.4.1.14519.5.2.1.6450.4007.2906.../ (104 slices)
```
## Usage
Install HoneyBee:
```bash
pip install honeybee-ml[all]
```
### Pathology — Load a whole-slide image
```python
from huggingface_hub import hf_hub_download
from honeybee.loaders.Slide.slide import Slide
from honeybee.processors.wsi import PatchExtractor
slide_path = hf_hub_download(
repo_id="Lab-Rasool/honeybee-samples",
filename="sample.svs",
repo_type="dataset",
)
slide = Slide(slide_path)
slide.detect_tissue(method="otsu")
patches = PatchExtractor(patch_size=256).extract(slide)
print(f"Extracted {len(patches)} patches from {slide.dimensions}")
```
### Clinical — Process a clinical PDF
```python
from huggingface_hub import hf_hub_download
from honeybee.processors import ClinicalProcessor
pdf_path = hf_hub_download(
repo_id="Lab-Rasool/honeybee-samples",
filename="sample.PDF",
repo_type="dataset",
)
processor = ClinicalProcessor()
result = processor.process(pdf_path)
print(result["entities"])
```
### Radiology — Download CT DICOM series
```python
from huggingface_hub import snapshot_download
ct_dir = snapshot_download(
repo_id="Lab-Rasool/honeybee-samples",
repo_type="dataset",
allow_patterns="CT/**",
)
```
## Citation
```bibtex
@article{rasool2024honeybee,
title={HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models},
author={Rasool, Ghulam and others},
journal={arXiv preprint arXiv:2405.07460},
year={2024}
}
```
## License
Apache 2.0 — see the [HoneyBee repository](https://github.com/Lab-Rasool/HoneyBee) for details.
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