PhD defence by Mathias Perslev
Summary
Diagnostic tasks in healthcare often involve segmenting regions of interest in images and time series, such as outlining organs in medical scans or scoring physiological events in electroencephalography (EEG) recordings. Medical professionals perform most of these complex and time-consuming tasks manually, leading to potential errors and limiting diagnostic efficiency. This thesis presents clinically robust and accurate machine learning models for segmenting medical image volumes and time series. Key practices for developing such models were identified: First, we reconfirmed that fully convolutional, feed-forward-only neural networks like the U-Net are broadly applicable as they performed well across diverse tasks in medical images and time series. Second, we found it beneficial to design data-augmentation techniques that induce various model invariance or equivariance properties to input data transformations that increase clinical robustness, even if the target function becomes more complex, as long as the augmentations also significantly expand the set of actual training examples.