Print Email Facebook Twitter Automated Machine Learning in Medical Image Segmentation Title Automated Machine Learning in Medical Image Segmentation Author van Gruijthuijsen, Coen (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Pan, W. (mentor) Starmans, Martijn (mentor) van Garderen, Karin (mentor) Klein, Stefan (graduation committee) Wisse, M. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering Date 2021-11-05 Abstract Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML) algorithm called Bayesian Optimization with HyperBand (BOHB). We found several hyperparameters significantly influencing the performance and confirmed that BOHB is applicable to these kind of problems. However, we did not find a significant difference between BOHB and Random Search on a small search, stressing the need of matching the search space to the search strategy. Finally, our approach was only slightly worse than the current state-of-the-art, which shows that AutoML has potential in this field. Subject Automated Machine LearningHyperparameter OptimizationConvolutional Neural Networks (CNNs)Semantic Segmentation To reference this document use: http://resolver.tudelft.nl/uuid:cd200c93-3185-4c5b-a3ef-4113b997cee1 Part of collection Student theses Document type master thesis Rights © 2021 Coen van Gruijthuijsen Files PDF THESIS_CSO_van_Gruijthuij ... _final.pdf 2.32 MB Close viewer /islandora/object/uuid:cd200c93-3185-4c5b-a3ef-4113b997cee1/datastream/OBJ/view