Datasetpublic

Lab-Rasool/honeybee-samples

HoneyBee Sample Files Sample data and resource files for the 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… See the full description on the dataset page: https://huggingface.co/datasets/Lab-Rasool/honeybee-samples.

sourceHugging Faceupdated 4mo agoView on Hugging Face

1likes100downloads

Dataset Card

HoneyBee Sample Files

Sample data and resource files for the 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 Package: `pip install 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. 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