Schoggi-Mimi/brain-mri-random-forest-segmentation
This project evaluates multiple Random Forest strategies for voxel-wise brain tissue segmentation from T1- and T2-weighted MRI, using a shared medical image analysis pipeline and comparing whether one unified model is better than splitting labels across multiple models.
Stars
—
Forks
—
Language
Python
License
Apache-2.0
Last pushed
Apr 05, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Schoggi-Mimi/brain-mri-random-forest-segmentation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fastapi/fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
scikit-learn/scikit-learn
scikit-learn: machine learning in Python
probabl-ai/skore
Track your Data Science. Skore's open-source Python library accelerates ML model development...
Farama-Foundation/Gymnasium
An API standard for single-agent reinforcement learning environments, with popular reference...
pallets/click
Python composable command line interface toolkit