Relaxing Supervision Requirements for Tomographic Data Analysis with Machine Learning

In this doctoral thesis, the power and potential of advanced imaging techniques, specifically Tomographic Imaging (hereinafter tomography), are explored in an era characterized by the rapid growth of data and the critical need for effective analysis strategies. This work engages with different modalities, such as but not limited to parallel beam X-ray Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). The research is centered around the incorporation of machine learning models, deep learning in particular, to optimize the analysis of tomography scans across various domains, including biology, medicine, and material sciences. This is achieved by navigating the primary challenges associated with the utilization of tomography, namely image preprocessing, data labeling, and model training. This work is organized as a series of chapters, consequently covering those topics in the order in which the proposed techniques would be applied in a practical pipeline of the data analysis.