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--- |
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task_categories: |
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- image-classification |
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- feature-extraction |
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tags: |
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- medical |
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- pathology |
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- radiology |
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- clinical |
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- oncology |
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- whole-slide-image |
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- dicom |
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- multimodal |
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size_categories: |
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- n<1K |
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|
pretty_name: HoneyBee Sample Files |
|
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--- |
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# HoneyBee Sample Files |
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Sample data for the [HoneyBee](https://github.com/Lab-Rasool/HoneyBee) framework — a scalable, modular toolkit for multimodal AI in oncology. |
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These files are used by the HoneyBee example notebooks to demonstrate clinical, pathology, and radiology processing pipelines. |
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**Paper**: [HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models](https://arxiv.org/abs/2405.07460) |
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**Package**: [`pip install honeybee-ml`](https://pypi.org/project/honeybee-ml/) |
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## Files |
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| File | Type | Size | Description | |
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|
|------|------|------|-------------| |
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| `sample.PDF` | Clinical | 70 KB | De-identified clinical report (PDF) for NLP extraction | |
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| `sample.svs` | Pathology | 146 MB | Whole-slide image (Aperio SVS) for tissue detection, patch extraction, and embedding | |
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| `CT/` | Radiology | 105 MB | CT scan with 2 DICOM series (205 slices total) for radiology preprocessing | |
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### CT Directory Structure |
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``` |
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CT/ |
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├── 1.3.6.1.4.1.14519.5.2.1.6450.4007.1209.../ (101 slices) |
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└── 1.3.6.1.4.1.14519.5.2.1.6450.4007.2906.../ (104 slices) |
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``` |
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## Citation |
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|
```bibtex |
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|
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). |
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|
https://doi.org/10.1038/s41746-025-02003-4 |
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|
``` |
