YashSharma/C2C
Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
This project helps pathologists and medical researchers automatically classify diseases from gigapixel-sized whole slide images (WSIs). It takes your WSI data, along with slide-level diagnostic labels, and outputs a prediction of disease presence for each WSI. This tool is designed for medical professionals working with digitized tissue samples who need to automate diagnosis.
No commits in the last 6 months.
Use this if you need to build a system that can accurately diagnose diseases from very large whole slide images, especially when only general slide-level labels are available for training.
Not ideal if you have detailed pixel-level annotations for every part of your tissue slides, as this tool is optimized for scenarios with less granular labeling.
Stars
59
Forks
9
Language
Python
License
MIT
Category
Last pushed
Oct 12, 2021
Commits (30d)
0
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