CODAIT/deep-histopath
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
This project helps pathologists and medical researchers automatically predict breast tumor proliferation scores from whole-slide histopathology images. It takes high-resolution digital scans of breast tumor tissue as input and outputs a proliferation score (1, 2, or 3), which indicates how fast cancer cells are growing. This automation aims to improve the consistency and speed of breast cancer diagnosis and prognosis.
208 stars. No commits in the last 6 months.
Use this if you need a more consistent and automated method to determine breast cancer proliferation scores from whole-slide histopathology images.
Not ideal if you are looking for a complete, production-ready diagnostic tool for immediate clinical use, as this project is still a work in progress.
Stars
208
Forks
89
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 07, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/CODAIT/deep-histopath"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
nilearn/nilearn
Machine learning for NeuroImaging in Python
aramis-lab/clinica
Software platform for clinical neuroimaging studies
TissueImageAnalytics/tiatoolbox
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
nipreps/mriqc
Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and...
nadeemlab/DeepLIIF
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification...