Exploring thehHyperparameter space of U-Net using genetic algorithms

Abstract

U-Net based architecture has become the de-facto standard approach for medical image segmentation in recent years. Many researchers have used the original U-Net as a skeleton for suggesting more advanced models such as UNet++ and UNet 3+. For our project, we also seek to optimize the original U-Net. Rather than changing the architecture itself, we optimize hyperparameters which does not affect the architecture, but affects the performance of the model. To optimize the hyperparameters, we use genetic algorithms. After the genetic algorithms have converged, we analyze the results and try to understand why the key factors behind explaining the performance.