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.
Visual Intelligence hosted over 45 international AI researchers for the DL2026 workshop at UiT The Arctic University of Norway.
Visual Intelligence will be well represented at MICCAI 2026, one of the leading AI conferences on medical imaging and computer assisted intervention, with two accepted research papers.
Visual Intelligence co-organized and attended the "Foundations of Arctic AI and Generative Forecasting": an inaugural workshop in the P1 Arctic AI program.
PhD Candidates Solveig Thrun and Christian Salomonsen attended the Datacloud Global Congress' Talent in Tech programme, which invited emerging talent to a unique programme designed to inspire, educate, and connect young professionals with senior leaders from global tech giants.
We warmly welcome Georgios Leontidis as a new Professor at SFI Visual Intelligence's hub at UiT The Arctic University of Norway.
Centre Director Robert Jenssen was interviewed by Norsk rikskringkasting (NRK) about the Japan-Norway AI Innovation Forum in Tokyo, Japan.
En ny AI-løsning utviklet ved UiT kan gjøre det enklere å avdekke feilrapportering og ulovlig fiske. Nå skal Norges Råfisklag teste teknologien i praktisk bruk (News article on kystogfjord.no)
Principal Investigator Kristoffer Wickstrøm was interviewed by Norsk rikskringkasting (NRK) about the Artificial Intelligence Day at UiT- The Arctic University of Norway.
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En ny, åpent tilgjengelig KI-modell kan endre hvordan geologer tolker seismikk. Den norske grunnmodellen lover raskere analyser, lavere terskel for innovasjon og nye måter å forstå undergrunnen på.
Centre Director Robert Jenssen was interviewed by Norsk rikskringkasting (NRK) about AI in fisheries and this year's UArctic (University of the Arctic) Congress on the Faroe Islands.
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Professor and AI expert Robert Jenssen is attending the Japan-Norway AI Innovation Forum and Japan-Norway Research Symposium, two high-level meetings with Norwegian and Japanese government leaders, business actors and researchers.
Visual Intelligence researchers Solveig Thrun and Kristoffer Wickstrøm took their research out of the lab and to Tromsø city centre as part of Pint of Science 2026.
Subsurface Digital Manager and former Visual Intelligence board member Cathrine Tegnander will serve as the elected leader of Visual Intelligence's board.
The Call for Papers and Abstracts for the Northern Lights Deep Learning (NLDL) Conference 2027 is officially announced – with submission deadlines on August 7th and Mid-September 2026 respectively.
The field of Visual Intelligence is continuously transforming. Chief Research Scientist Arnt-Børre Salberg dives deeper into the current trends in the field of visual intelligence as of early 2026.
Generativ kunstig intelligens er imponerende, men ikke alltid så nyttig til å løse industrielle problemer (Norwegian op-ed in digi.no).
Congratulations to Marit Dagny Kristine Jenssen, who successfully defended her PhD thesis at UiT The Arctic University of Norway on April 10th
The NCS model, a seismic foundation model trained on data from the Norwegian data repository for subsurface data, is now available as an open-source model, allowing anyone to download, utilize, and further develop the model.
The Visual Intelligence Annual Report 2025, highlighting the centre's progress, activities, achieved innovations, staff, funding, and publications for 2025, is now available to read on our websites.
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Congratulations to Centre Director Robert Jenssen, who received UiT's Research and Development Award at the university's annual celebration.