DIAGNijmegen/pathology-he-auto-augment
H&E tailored Randaugment: automatic data augmentation policy selection for H&E-stained histopathology.
This project helps medical researchers and pathologists improve the reliability of AI models used for analyzing H&E-stained histopathology slides. It takes raw digital pathology images and processes them to create a more diverse and robust training dataset. The output is augmented image data that helps AI models perform consistently, even when there are variations in how the slides were prepared or scanned.
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Use this if you are building or training deep learning models for pathology image analysis and need to make them more robust to variations in tissue staining and image acquisition.
Not ideal if your image analysis task does not involve H&E-stained histopathology, or if you need to perform real-time image analysis rather than model training.
Stars
57
Forks
7
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 20, 2023
Commits (30d)
0
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