v0.2.0
honeybee-ml v0.2.0
Highlights
- Published in Nature Digital Medicine -- HoneyBee paper now published at nature.com/articles/s41746-025-02003-4
- 313 tests with comprehensive mocked test suite (no GPU or model weights required)
New Features
WSI Pipeline (Pathology)
- New Slide class with backend abstraction (auto-detects CuCIM or OpenSlide)
- PatchExtractor for grid-based patch extraction with tissue filtering
- Patches container with quality scoring, stain normalization, and immutable filtering
- TissueDetector deep learning model for tissue segmentation
- Backend abstraction layer (_backend.py) normalizing CuCIM vs OpenSlide APIs
Clinical NLP
- Fully rewritten modular clinical processor (honeybee/processors/clinical/)
- Document ingestion pipeline with PDF/OCR support
- NER engine with pluggable backends (scispaCy, MedSpaCy, MedCAT, Transformers)
- Ontology mapping via UMLS, BioPortal, and SNOMED CT (Snowstorm) clients
- FHIR converter and HL7 parser for clinical interoperability
- Temporal timeline extraction
- Embedding engine with local and API backends
Model Registry
- New model registry system (honeybee/models/registry/) with protocol-based provider architecture
Radiology
- Overhauled preprocessing, segmentation, and processor modules
- Removed legacy radiology loaders (functionality moved to processors)
Bug Fixes
- Pathology processor bug fixes for embedding generation and aggregation
- Clinical processor implementation fixes
- Stain normalization cleanup (removed _fixed and _working variants)
Documentation
- Simplified README
- Expanded docs for clinical, pathology, and radiology processing
- New pathology and radiology visualization assets on website
Breaking Changes
- honeybee/processors/clinical_processor.py removed -- replaced by honeybee/processors/clinical/ package
- honeybee/loaders/Radiology/ removed -- use honeybee/processors/radiology/ instead
- honeybee/loaders/Scans/ removed
- Legacy PathologyProcessor methods (load_wsi, detect_tissue, extract_patches, etc.) now emit DeprecationWarning and delegate to new classes
Install
pip install honeybee-ml==0.2.0