PhD defence by Mathias Perslev
Segmentation of medical images and time series using fully convolutional neural networks
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. With the increasing global diagnostic burden on healthcare systems, there is a growing need for (semi-) automatic computer systems to alleviate repetitive manual tasks. Furthermore, these systems can make expert knowledge available for people with limited access to well-trained medical doctors.
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. Finally, we found clinical robustness achievable by training machine learning models on extensive and highly variable training datasets from multiple sources, even if datasets differ in recording hardware, patient population, or data preprocessing pipeline.
Principal Supervisor Christian Igel
Associate Professor Melanie Ganz-Benjaminsen, DIKU
Professor Klaus-Robert Müller, TU Berlin
Professor M. Brandon Westover, Harvard Medical School
For an electronic copy of the thesis, please visit the PhD Programme page.