Celebrating the defence
Stine Hansen defended her PhD thesis “Leveraging Supervoxels for Medical Image Volume Segmentation With Limited Supervision” on Dec. 16th at UiT The Arctic University of Norway.
The focus of Stine Hansens thesis is on the development of new ML algorithms for medical image segmentation with limited supervision. In particular, completely unsupervised solutions to lung tumor segmentation and solutions that only require a few labeled samples to perform organ segmentation (few-shot learning) are proposed. To exploit the data-specific opportunities of medical images, automatically generated supervoxels are exploited in various ways to solve key challenges related to the task.
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Evaluation Committee
Supervisors
The majority of existing methods for machine learning-based medical image segmentation are supervised models that require large amounts of fully annotated images. These types of datasets are typically not available in the medical domain and are difficult and expensive to generate. A wide-spread use of machine learning based models for medical image segmentation therefore requires the development of data-efficient algorithms that only require limited supervision. To address these challenges, this thesis presents new machine learning methodology for unsupervised lung tumor segmentation and few-shot learning based organ segmentation. When working in the limited supervision paradigm, exploiting the available information in the data is key. The methodology developed in this thesis leverages automatically generated supervoxels in various ways to exploit the structural information in the images. The work on unsupervised tumor segmentation explores the opportunity of performing clustering on a population-level in order to provide the algorithm with as much information as possible. To facilitate this population-level across-patient clustering, supervoxel representations are exploited to reduce the number of samples, and thereby the computational cost. In the work on few-shot learning-based organ segmentation, supervoxels are used to generate pseudo-labels for self-supervised training. Further, to obtain a model that is robust to the typically large and inhomogeneous background class, a novel anomaly detection-inspired classifier is proposed to ease the modelling of the background. To encourage the resulting segmentation maps to respect edges defined in the input space, a supervoxel-informed feature refinement module is proposed to refine the embedded feature vectors during inference. Finally, to improve trustworthiness, an architecture-agnostic mechanism to estimate model uncertainty in few-shot segmentation is developed. Results demonstrate that supervoxels are versatile tools for leveraging structural information in medical data when training segmentation models with limited supervision.
We congratulate PhD Fellow Suaiba Salahuddin, who will start as new Head Engineer at the Climate-Ecological Observatory for Arctic Tundra (COAT)
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Researchers at Visual Intelligence have developed AI to predict the risk of getting breast cancer within one to five years. It can help detect more cases of breast cancer earlier.
Visual Intelligence researchers contributed with various activities during Forskningsdagene 2026: Norway's annual research festival.
100 researchers, user partners and invited guests gathered in Oslo, Norway for the Visual Intelligence Days: the centre's annual conference for sharing knowledge and updates on the latest research and innovations.
Why should we think twice before using AI as a therapist, romantic partner, or to help out with homework? AI expert Elisabeth Wetzer explains (Popular science article on sciencenorway.no / forskning.no).
Associate Professor Kristoffer Wickstrøm was interviewed by Norsk rikskringkasting (NRK) about the ongoing debate on whether to halt current and future AI development
Representatives from the Research Council of Norway came to Visual Intelligence's hub in Tromsø for a site visit meeting.
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Harald Lykke Joakimsen, a PhD Candidate at Visual Intelligence's hub in Tromsø, will compete in Forsker Grand Prix: Norway's national popular science competition.
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Congratulations to Centre Director Robert Jenssen, who was appointed as a new fellow of the International Associaton for Pattern Recognition (IAPR).
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Why should we think twice before using AI as a therapist, romantic partner, or to help with homework? AI expert Elisabeth Wetzer explains.
Athinoulla Konstantinou is a PhD student at the University of Aberdeen. She visited Visual Intelligence's hub in Tromsø to work with equivariant modelling.
Visual Intelligence gathered experts from Norwegian politics, research council, industry, the public sector, and academia to discuss the value of Norwegian AI competence and Norwegian-developed models.
Visual Intelligence researchers are attending two AI-oriented events at Arendalsuka 2026: Norway's largest annual political festival.
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)