---
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)
```

## Citation

```bibtex
Tripathi, A., Waqas, A., Schabath, M.B. et al. HONeYBEE: enabling scalable multimodal AI in
oncology through foundation model-driven embeddings. npj Digit. Med. 8, 622 (2025).
https://doi.org/10.1038/s41746-025-02003-4
```