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.
María Castro Fernández is a PhD Candidate at the Research Institute of Applied Microelectronics at Universidad de Las Palmas de Gran Canaria, in Spain. She visited Visual Intelligence in Tromsø from January to June 2024.
AI tools wield enormous potential within the health sector. How do we ensure that such tools are safely implemented and used within this field? The event will take place on August 14th, 11:45-12:45 at Bankgården, Strandgaten 2-4.
Thea Brüsch is a PhD student at the Section for Cognitive Systems at the Technical University of Denmark (DTU). She visited the UiT Machine Learning Group from January to June 2024.
Visual Intelligence happily welcomes Lars Uebbing, who recently started as a doctoral research fellow at Visual Intelligence in Tromsø.
Det tar for lang tid å analysere bilder for å finne ut om en pasient har kreft i leveren. Nå har forskerne laget et system som gjør jobben mer effektivt (Norwegian news story).
We are happy to announce that a Visual Intelligence paper "The 3-billion fossil question: How to automate classification of microfossils" was featured in the scientific newspaper EurekAlert!
In the episode, Østmo was included in an Abels tårn expert panel to answer different questions about AI and deep learning.
Visual Intelligence organized a special curricular course on Generative AI at UiT The Arctic University of Norway this spring. The course covered Large Language Models, Generative AI for images, and Generative AI for audio.
Our sponsorship packages provide a number of benefits, such as registration and networking benefits for company employees, company and brand visibility, and more.
Associate professor and principal investigator Kristoffer Wickstrøm recently talked about a collaborative Visual Intelligence project between UiT and UNN on NRK Radio. The project aims to develop AI algorithms to detect liver cancer on CT images.
We congratulate Tollef Jahren for successfully defending his PhD thesis "Deep learning for detecting valvular events and suppressing reverberations in cardiac ultrasound" for the degree Philosophiae Doctor at Department of Informatics, University of Oslo.
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Noen få tastetrykk er alt som skal til for å finne falske nakenbilder og videoer av norske kjendiser. – Ekstremt bekymringsfullt, sier KI-ekspert
Visual Intelligence congratulates Daniel Johansen Trosten for receiving the Dissertation Award of 2023 from Norwegian Artificial Intelligence Society (NAIS)!
Centre director Robert Jenssen represented Visual Intelligence at the marine conference Blått kompass.
The Visual Intelligence Graduate School (VIGS) recently gathered our Tromsø based researchers at Árdna - The Sami Cultural Center for a social event about exchange stays.
We congratulate professor Michael Kampffmeyer for receiving the "Early career investigator" award at the NORA Annual Awards!
Til tross for gode karakterer, søker svært få jenter seg til studier innen matematikk, naturfag og teknologi. En studie viser at det kan handle om lav selvtillit.
We invite submissions presenting new and original research on all aspects of Deep Learning for the Northern Lights Deep Learning Conference (NLDL) 2025.
I am eager to absorb knowledge from these experts and contribute to our collective understanding, says Filippo Bascetta, who is one of two new VI interns.
Listen to VI co-director Line Eikvil and research director Anders Løland (Norsk Regnesentral) talk about developments before and after the breakthrough of deep learning in the NR podcast, Sannsynligvis VIKTIG (Norwegian dialogue).