mahmoodlab/TRIDENT
Toolkit for large-scale whole-slide image processing.
This tool helps pathologists and researchers analyze large collections of whole-slide images (WSIs) for computational pathology studies. It takes digital pathology slides as input and automatically identifies tissue, extracts relevant image patches, and generates numerical features (embeddings) from those patches and the entire slide. This is ideal for medical researchers or computational pathologists who need to process many histology images for AI model training or quantitative analysis.
502 stars. Actively maintained with 5 commits in the last 30 days.
Use this if you need to efficiently prepare large numbers of whole-slide pathology images for AI model development or quantitative analysis, automating tasks like tissue segmentation and feature extraction.
Not ideal if you only need to view or manually annotate a few individual slides, or if your primary focus is on standard diagnostic reporting without computational analysis.
Stars
502
Forks
110
Language
Python
License
—
Category
Last pushed
Mar 06, 2026
Commits (30d)
5
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