Using pretrained generators, the library allows to quickly genrate synthetic data that can be used in experiments. Below is a simple example that generates a dataset of e.g. 1000 synthetic polyp images with segmentation marks:
Step 1: pip install medigan
Step 2: python3
from medigan import Generators
Generators().generate(model_id=10, num_samples=1000 install_dependencies=True)
A paper describing the library in detail was written in collaboration with the University of Arkansas for Medical Sciences which can be found here.