BMIRDS/HistoGAN
Code for "Generative Image Translation for Data Augmentation in Colorectal Histopathology Images" full paper at ML4H Workshop at NeurIPS 2019.
This tool helps medical researchers and pathologists enhance their datasets of colorectal histopathology images for machine learning. By taking a collection of real histopathology images, it generates diverse synthetic images. This allows users to create larger and more varied training sets for building more robust image classification models for diagnostic tasks.
No commits in the last 6 months.
Use this if you need to augment a limited dataset of colorectal tissue images to improve the performance of a machine learning classifier, especially when real data collection is challenging.
Not ideal if your primary goal is to interpret individual image features, as the generated images are synthetic and intended for dataset expansion rather than detailed analysis.
Stars
71
Forks
20
Language
Python
License
GPL-3.0
Category
Last pushed
Feb 03, 2021
Commits (30d)
0
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