| --- |
| 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 |
| ``` |
