Generative augmentation methods for more robust wound segmentation models.
Our aim: To use generative methods to reduce inherent biases in data and improve generalisability of models
Demo notebooks/scripts are provided, implementing two generative networks (pix2pix and Augmented CycleGAN). These networks are used to generate synthetic wound images, with tune-able latent spaces for controlling wound attributes.
Generated data should replace/be combined with the original dataset, and placed in the project root directory (see sample images).
A segmentation network is then trained and evaluated to compare the performance with and without augmenting the original training dataset.