Richarizardd/Self-Supervised-ViT-Path
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)
This project helps pathologists and medical researchers analyze whole slide images (WSIs) of tissue samples more effectively. It takes digital pathology images as input and generates detailed visual concepts and classifications for different tissue types and cell locations. The output provides enhanced insights into disease characteristics, aiding in tasks like cancer diagnosis and subtyping.
143 stars. No commits in the last 6 months.
Use this if you need to extract meaningful features and localize specific visual concepts within histopathology images, particularly when dealing with large datasets and varying staining protocols.
Not ideal if you are looking for a plug-and-play diagnostic tool without needing to integrate it into an existing computational pathology workflow.
Stars
143
Forks
19
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jun 09, 2022
Commits (30d)
0
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