Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming

In this contribution we study how to effectively evolve programs tailored for biomedical image segmentation by using an Active Learning approach in Cartesian Genetic Programming (CGP). Active Learning allows to dynamically select training data by identifying the most informative next image to add to the training set. We study how different metrics for selecting images under active learning impact the searchability of CGP. Our results show that datasets built during evolution with active learning improve the performance of Cartesian GP substantially. In addition, we found that the choice of the particular metric used for selecting which images to add heavily impacts convergence speed. Our work shows that the right choice of the image selection metric positively impacts the effectiveness of the evolutionary algorithm.