Dr. Wickstrøm together with his opponents, supervisors and representatives from UiT after his defense.
Image:
Changkyu Choi.

Dr. Wickstrøm together with his opponents, supervisors and representatives from UiT after his defense.

Successful PhD defense in the Visual Intelligence research centre

Kristoffer Wickstrøm defended his PhD thesis “Advancing deep learning with emphasis on data-driven healthcare” on Oct 28 at UiT The Arctic University of Norway.

Successful PhD defense in the Visual Intelligence research centre

Kristoffer Wickstrøm defended his PhD thesis “Advancing deep learning with emphasis on data-driven healthcare” on Oct 28 at UiT The Arctic University of Norway.

Wickstrøm has in his thesis developed a range of novel deep learning methods aiming at producing a new level of interpretability and uncertainty quantification for health-related applications within medical computer vision and analysis of patient data from electronic health records.

Wickstrøm presents his new interpretability method called “RELAX” during his PhD defense. Image credit: Changkyu Choi.

Wickstrøm defended his work publicly and under the scrutiny of his opponents, Irina Voiculescu, University of Oxford, and Lars Kai Hansen, Technical University of Denmark

Prior to the actual defense, Wickstrøm gave his so-called trial lecture (a requirement in Norway) entitled "Technical aspects of translating AI algorithms into real life medical practice, within the design and implementation of Randomized Controlled Trials".

From left: Leader of the PhD defense Prof. Olav G. Hellesø, head of Dept. Physics and Technology, UiT; Assoc. Prof. Benjamin Ricaud, internal member and leader of the evaluation committee, UiT; Assoc. Prof. Irina Voiculescu, University of Oxford; Kristoffer Wickstrøm; Prof. Lars Kai Hansen, Technical University of Denmark; Prof. Robert Jenssen, UiT; Assoc. Prof Michael Kampffmeyer, UiT; Assoc. Prof. Karl Øyvind Mikalsen, University Hospital of North Norway and UiT – all supervisors. Image credit: Harald Lykke Joakimsen.

Summary of Wickstrøm’s thesis work:

The right to health is a fundamental human right, but numerous challenges face those who wish to comply. Shortage of trained health personnel, increases in costs, and an aging population are just a few examples of obstacles that arise in the healthcare sector. Tackling such problems is crucial to provide high quality and reliable healthcare to people around the world. Many researchers and healthcare professionals believe that data-driven healthcare has the potential to solve many of of these problems. Data-driven methods are based on algorithms that learn to perform tasks by identifying patterns in data, and often improve in line with the amount of data. A key driving force in contemporary data-driven healthcare is deep learning, which is part of the representation learning field where the goal is to learn a data representation that is beneficial for performing some task. Deep learning has lead to major improvements in important healthcare domains such as computer vision and natural language processing. However, deep learning algorithms lack explainability, do not provide a notion of uncertainty, and struggle when tasked with learning from unlabeled data. These are fundamental limitations that must be tackled for deep learning-based data-driven healthcare to reach its full potential. Towards tackling these limitation, we propose new methodology within the field of deep learning. We present the first methods for capturing uncertainty in explanations of predictions, and we introduce the first framework for explaining representations of data. We also introduce a new method that utilizes domain knowledge to extract clinically relevant features from medical images. While our emphasis is on healthcare applications, the proposed methodology can be employed in other domains as well, and we believe that the innovations in this thesis can play an important part in creating trustworthy deep learning algorithms that can learn from unlabeled data.

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