Expensive optimization with model-based evolutionary algorithms applied to medical image segmentation using deep learning

Recently great achievements have been obtained with Artificial Intelligence (AI) methods including human-level performance in such challenging areas as image processing, natural language processing, computational biology, and game playing. Arguably, one of the most societally important application fields of such methods is healthcare. AI is a broad term, which in general refers to systems and methods (components of systems), capable of solving complex tasks and ultimately doing it autonomously, i.e., without human participation, or, if necessary (e.g., in healthcare) with some human supervision. Machine Learning (ML) is a subfield of AI that consists of diverse methods which utilize available data to extractmeaningful and actionable knowledge.

Three key factors have contributed to the recent success of ML methods:

1) Novel algorithms;

2)Highly efficient hardware, the computational capabilities of which are perfectly aligned with the currently most popular component of AI systems - deep neural networks (a computational abstraction that vaguely resembles a brain and can be efficient in solving differentML problems);

3) Huge amounts of digitally available data which can be used to train ML models. In this thesis, we mainly focus on the combination of algorithm development and data-related aspects....