srinidhiPY/SSL_CR_Histo
Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medical Image Analysis (MedIA) Journal, Oct, 2021.
This project helps medical researchers and pathologists efficiently analyze large histopathology whole-slide images (WSIs) for tasks like tumor detection and tissue classification. It takes unlabeled and limited labeled WSI data as input and produces highly accurate predictions for diagnosing various conditions, even with minimal expert annotations. This tool is designed for medical researchers, pathologists, or computational biologists working with histopathology image analysis.
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Use this if you need to train robust models for histopathology image analysis tasks like tumor detection or tissue classification, especially when you have a lot of unlabeled WSI data but limited expert annotations.
Not ideal if your primary data source is not whole-slide histopathology images or if you have ample labeled data and do not require methods for annotation efficiency.
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Python
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MIT
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Mar 05, 2022
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