histocartography
importedsoftware/histocartography
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- AGPL-3.0(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- BiomedSciAI
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/BiomedSciAI/histocartography
- Documentation
- unknown
- Tags
- deep-learning · graph-neural-networks · healthcare · machine-learning · pathology · pytorch
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- torchvahadanepathology · pytorch
Gpu accelerated vahadane stain normalization for Digital Pathology workflows.
- GNN_for_EHRgraph-neural-networks · pytorch
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
- medical-imaginghealthcare · pathology
Accelerate ingestion/transformation of pathology images into DICOMWeb
- torchdruggraph-neural-networks · pytorch
A powerful and flexible machine learning platform for drug discovery
- anonymize-slide-python3-guipathology
Delete the label image to deidentify a whole-slide image (WSI) with an optional GUI
- Dermoscopic-Image-ICL-GPT4pathology
Dermoscopic Image In-Context Learning (ICL) with GPT4v
- api.github.com/repos/BiomedSciAI/histocartographyretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2023-12-23, 273 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/30.json→ .entries["histocartography"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.