Dr. Wickstrøm together with his opponents, supervisors and representatives from UiT after his defense.
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.
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 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".
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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Congratulations to associate professor Ali Ramezani-Kebrya for receiving a prestigious FRIPRO grant from the Research Council of Norway!
KI har en tendens til å behandle menn og kvinner ulikt. Ekspert mener teknologien reproduserer gamle fordommer (Norwegian news story in NRK.no)
To UiT-forskere ser store etiske og juridiske problemer ved bruk av kunstig intelligens i kommunal saksbehandling (Norwegian news story in Nordlys.no).
Michael Kampffmeyer is one of UiT's youngest professors and has already distinguished himself through his contributions to AI research. He has now won UiT's award for young researchers at the university's annual celebration.
The fifth Visual Intelligence annual report, showcasing the centre's activities, results, staff, funding and publications for 2024, is now available on our web pages.
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I juni møtes det norske KI-miljøet i Tromsø for å presentere ny forskning og diskutere nye retninger innen feltet. KI-forskere inviteres til å delta og vise fram forskningen sin under konferansen.
Visual Intelligence and the Digital Innovation Lab invited industry professionals to present ideas for master's projects to computer science and machine learning students at UiT The Arctic University of Norway.
Kunstig intelligens (KI) endrer måten vi løser komplekse problemer på. Ved UiT Norges arktiske universitet leder professor Robert Jenssen Visual Intelligence, et senter for forskningsdrevet innovasjon som utvikler neste generasjons KI-metoder.
Visual Intelligence researchers had the great pleasure of talking to high school students about the AI study programme at UiT The Arctic University of Norway during the UiT Open Day.
Director Robert Jenssen represented Visual Intelligence at the CuttingEdgeAI seminar "KI anno 2023: I offentlighetens interesse?" at the University of Bergen on March 7th.
Det hender at kunstig intelligens behandler menn og kvinner ulikt. Hvordan skjer dette? KI-forsker Elisabeth Wetzer forklarer hva som ligger bak skjevhetene i teknologien (Popular science story in forskning.no and sciencenorway.no)
Congratulations to Visual Intelligence board member Viktoria Gulliksen Stray for being featured in Abelia and ODA-Nettverk's list of Top 50 Influential Tech Women in Norway 2025!
Norwegian Minister of Research and Higher Education, Sigrun Aasland (Ap) got to hear about Visual Intelligence's work toward developing the next generation of deep learning methodology at a visit to UiT in Tromsø
Hva er lov og hva er ikke lov? Det er blant spørsmålene studentene stiller seg. (NRK.no story featuring director Robert Jenssen)
We had the pleasure of talking to students about the exciting career opportunities at Visual Intelligence and UiT The Arctic University of Norway during TEKdagen 2025
Last week, we welcomed students from Breivang upper secondary school to a full-day practical session on AI and programming at UiT The Arctic University of Norway
We happily welcome Solveig Thrun, who recently started as a doctoral research fellow at Visual Intelligence in Tromsø in January 2025.
Visual Intelligence and UiT researchers organized a special curricular course on collaborative coding and reproducible research at UiT The Arctic University of Norway.
Andrew McDonald, a PhD student from University of Cambridge and the British Antarctic Survey, presented his work on sea ice forecasting with diffusion models, as well as the downstream applications of those forecasts, at Visual Intelligence in Tromsø.
Deep learning researchers from 27 different countries congregated at the 8th edition of the NLDL conference. - I have seen several interesting posters and talks that have opened my mind about different paths to explore in my own research, says NLDL 2025 attendee Tiago Ramos.