Kristoffer Knutsen Wickstrøm

Disputas - Kristoffer Knutsen Wickstrøm

Master of science Kristoffer Knutsen Wickstrøm will on October 28th at 12.15 publically defend his PhD degree in science.

Title of the PhD thesis:

Advancing deep learning with emphasis on data-driven healthcare

Abstract:

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.

The thesis is published and available in Munin

Supervisors

  • Professor Robert Jenssen, IFT, UiT (main supervisor)
  • Førsteamanuensis Michael Kampffmeyer, IFT, UiT (co-supervisor)
  • Associate professor Karl Øyvind Mikaelsen, IKM, UiT (co-supervisor)

Evaluation committee

  • Associate professor Irina Voiculescu, University of Oxford, UK (1. Opponent)
  • Professor Lars K. Hansen, Technical University of Denmark, (2. Opponent)
  • Professor Benjamin Ricaud, IFT, UiT (intern member and leader of the committee)

Links to the trial lecture and defense will be possible to open when the live stream begins. If you haven`t clicked the link to the folder before it begins, refresh the web browser for them to become visible. If you have clicked the link to the trial lecture or defense before it has started, it will open automatically when the stream begins. 

Link to folder which contains trial lecture and defense

The trial lecture starts at 10.15 October 28th

Trial lecture

The defense starts at 12.15 October 28th

Defense

Når: 28.10.22 kl 12.15–15.00
Hvor: Realfagsbygget B302, store auditorium
Sted: Digitalt, Tromsø
Målgruppe: Ansatte, Studenter, Gjester / eksterne, Inviterte, Enhet
Kontakt: Eirik Derås Verlo
Telefon: 92862675
E-post: eve012@uit.no
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