agentdr1/LA_MIL
Implementation of LA_MIL, Local Attention Graph-based Transformer for WSIs, PyTorch
This project helps medical researchers and pathologists analyze large whole slide images (WSIs) of tissue samples to predict genetic alterations or other biomarkers from cancer histology. It takes raw WSI data, processes it into features, and outputs predictions about specific mutations or characteristics present in the tissue. This tool is designed for scientists and clinicians working with digital pathology.
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Use this if you need to predict multiple genetic markers or other clinical outcomes directly from whole slide images of cancer tissue, especially if you want to explore different attention mechanisms.
Not ideal if you are not working with whole slide images for biomarker prediction, or if you need a pre-packaged solution that doesn't require setting up a machine learning pipeline.
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Language
Python
License
MIT
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Last pushed
Oct 13, 2023
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