HoneyBee

A Scalable Modular Framework for Multimodal AI in Oncology

Documentation | Paper | Examples | Demo | Google Colab

📰 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

🏗️ Modular Architecture

• 3-Layer Design: Clean separation between data loaders, embedding models, and processors
• Unified API: Consistent interface across all modalities
• Extensible: Easy to add new models and data sources
• Production-Ready: Optimized for both research and clinical deployment

📊 Comprehensive Data Support

Medical Imaging

• Pathology: Whole Slide Images (WSI) - SVS, TIFF formats with tissue detection
• Radiology: DICOM, NIFTI processing with 3D support
• Preprocessing: Advanced augmentation and normalization pipelines

Clinical Text

• Document Processing: PDF support with OCR for scanned documents
• NLP Pipeline: Cancer entity extraction, temporal parsing, medical ontology integration
• Database Integration: Native MINDS format support
• Long Document Handling: Multiple tokenization strategies for clinical notes

Molecular Data

• Genomics: Support for expression data and mutation profiles
• Integration: Seamless combination with imaging and clinical data

🧠 State-of-the-Art Embedding Models

Clinical Text Embeddings

• GatorTron: Domain-specific clinical language model
• BioBERT: Biomedical text understanding
• PubMedBERT: Scientific literature embeddings
• Clinical-T5: Text-to-text clinical transformers

Medical Image Embeddings

• REMEDIS: Self-supervised medical image representations
• RadImageNet: Pre-trained radiological feature extractors
• UNI: Universal medical image encoder
• Custom Models: Easy integration of proprietary models

🛠️ Advanced Capabilities

Multimodal Integration

• Cross-Modal Learning: Unified representations across modalities
• Attention Mechanisms: Interpretable fusion strategies
• Patient-Level Aggregation: Comprehensive patient profiles

Analysis Tools

• Survival Analysis: Cox PH, Random Survival Forest, DeepSurv
• Classification: Multi-class cancer type prediction
• Retrieval: Similar patient identification
• Visualization: Interactive t-SNE dashboards

Clinical Applications

• Risk Stratification: Patient outcome prediction
• Treatment Planning: Personalized therapy recommendations
• Biomarker Discovery: Multi-omic pattern identification

🚀 Quick Start

Prerequisites

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.7+ (optional, for GPU acceleration)

System Dependencies

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

# macOS
brew install openslide tesseract

# Windows
# Install from official websites:
# - OpenSlide: https://openslide.org/download/
# - Tesseract: https://github.com/UB-Mannheim/tesseract/wiki

Installation

From PyPI (Recommended)

# Install the base package
pip install honeybee-ml

# Download required NLTK data for clinical processing
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab')"

From Source (Development)

# Clone the repository
git clone https://github.com/lab-rasool/HoneyBee.git
cd HoneyBee

# Install dependencies
pip install -r requirements.txt

# Download required NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab')"

# Install in development mode
pip install -e .

Environment Setup

Create a .env file in the project root:

# MINDS database credentials (if using MINDS format)
HOST=your_server
PORT=5433
DB_USER=postgres
PASSWORD=your_password
DATABASE=minds

# HuggingFace API (for some models)
HF_API_KEY=your_huggingface_api_key

🔬 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

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Fork and clone your fork
git clone https://github.com/lab-rasool/HoneyBee.git
cd HoneyBee

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -r requirements.txt
pip install -e .

🐛 Known Issues & Limitations

• Alpha Status: Some features are still under development
• Memory Requirements: WSI processing requires significant RAM (16GB+ recommended)
• GPU Recommended: While CPU fallback exists, GPU acceleration significantly improves performance
• Limited Test Coverage: Comprehensive test suite is planned for future releases

📜 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