honeybee-ml 0.2.1

A Scalable Modular Framework for Multimodal AI in Oncology

HoneyBee

A Scalable Modular Framework for Multimodal AI in Oncology

Documentation & Examples | Paper

Publication

HoneyBee has been officially published in Nature Digital Medicine!

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

Overview

HoneyBee is a comprehensive multimodal AI framework designed specifically for oncology research and clinical applications. It seamlessly integrates and processes diverse medical data types—clinical text, radiology images, pathology slides, and molecular data—through a unified, modular architecture. Built with scalability and extensibility in mind, HoneyBee empowers researchers to develop sophisticated AI models for cancer diagnosis, prognosis, and treatment planning.

[!WARNING] Alpha Release: This framework is currently in alpha. APIs may change, and some features are still under development.

Key Features

• Multimodal data support: clinical text, radiology (DICOM/NIFTI), pathology (WSI), and molecular data
• 3-layer modular architecture: clean separation between loaders, processors, and embedding models
• Clinical NLP pipeline: OCR, cancer entity extraction, temporal parsing, and medical ontology mapping
• Whole Slide Image processing: tissue detection, patch extraction, stain normalization, and quality filtering
• State-of-the-art embedding models: GatorTron, BioBERT, PubMedBERT, UNI, REMEDIS, RadImageNet, and more
• Cross-modal integration: unified patient-level representations from multiple data modalities
• Survival analysis: Cox PH, Random Survival Forest, and DeepSurv
• Similar patient retrieval: find patients with matching clinical profiles
• Interactive visualization: t-SNE dashboards for embedding exploration
• GPU-accelerated: CuCIM backend for WSI processing with OpenSlide fallback

Quick Start

System Dependencies

# Ubuntu/Debian
sudo apt-get install -y openslide-tools tesseract-ocr

# macOS
brew install openslide tesseract

Installation

pip install honeybee-ml
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab')"

Optional Extras

Extra	Command	Includes
Clinical	pip install honeybee-ml[clinical]	NLP, OCR, and text processing dependencies
Pathology	pip install honeybee-ml[pathology]	WSI loading and image processing
Molecular	pip install honeybee-ml[molecular]	Genomics and expression data support
All	pip install honeybee-ml[all]	Everything above

Research Applications

HoneyBee has been successfully applied to:

• Cancer Subtype Classification: Automated identification of cancer subtypes from multimodal data
• Survival Prediction: Risk stratification and outcome prediction for treatment planning
• Similar Patient Retrieval: Finding patients with similar clinical profiles for precision medicine
• Biomarker Discovery: Identifying multimodal patterns associated with treatment response

License

See the LICENSE file for details.

Citation

If you use HoneyBee in your research, please cite our paper:

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