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.
Senior Researcher Fredrik Dahl recently gave a talk about Norsk Regnesentral's work on developing AI algorithms for automatic analysis of image quality and cancer detection at Norsk Radiografforbund's mammography symposium in Oslo.
Han høster anerkjennelse for sitt forskningsarbeid innen utvikling av mer effektive KI-modeller (Norwegian news article in Nordlys).
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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.