---
task_categories:
  - image-classification
  - feature-extraction
tags:
  - medical
  - pathology
  - radiology
  - clinical
  - molecular
  - multi-omics
  - oncology
  - whole-slide-image
  - dicom
  - multimodal
size_categories:
  - n<1K
pretty_name: HoneyBee Sample Files
---

# HoneyBee Sample Files

Sample data and resource files 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 (clinical, pathology, radiology) and by HoneyBee's molecular processing code at runtime (`Hugo_symbols.tsv` is fetched on first use of DNA mutation preprocessing).

**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 |
| `Hugo_symbols.tsv` | Molecular | 128 KB | Hugo Gene Symbol vocabulary (17,312 symbols, one per line, no header) used by SeNMo's DNA mutation preprocessing. Ported from [lab-rasool/SeNMo](https://github.com/lab-rasool/SeNMo). Fetched automatically by `honeybee.processors.molecular.preprocessing.preprocess_dna_mutation()` on first use. |

### 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
```

If your work uses `Hugo_symbols.tsv` (the molecular sample), also cite the SeNMo paper that originally curated this vocabulary:

```bibtex
Waqas, A., Tripathi, A., Ahmed, S. et al. Self-Normalizing Multi-Omics Neural Network for
Pan-Cancer Prognostication. Int. J. Mol. Sci. 26, 7358 (2025).
https://doi.org/10.3390/ijms26157358
```