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.
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.
NRK, the national broadcasting company in Norway, spoke to Visual Intelligence researchers Robert Jenssen, Suaiba Amina Salahuddin, Michael Kampffmeyer and Elisabeth Wetzer about this year's Northern Lights Deep Learning Conference in Tromsø, Norway.
Andre uka i januar samles forskere fra hele verden i Tromsø for å dele og lære om siste nytt innen forskning på kunstig intelligens.
In record time, principal investigator Michael Kampffmeyer became a professor in machine learning at the young age of 32. Read more about his academic journey and work in the latest UiT Researcher Portrait
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2024 has been a year full of exciting events and accomplishments, and we look forward to continuing our journey on researching the next generation of deep learning methodology in 2025!
In a Dagens Medisin op-ed, SPKI director Karl Øyvind Mikalsen, Kristine Bø, Bjorn Anton Graff and Kurt Vanvik discuss how interregional collaborations in Norway now contributes to the implementation of AI radiology solutions across the Norwegian health regions
Jenssen has established several important collaborations between Visual Intelligence and international research environments within artificial intelligence. He has now joined the scientific advisory committee at Europe's most prominent research centre within intelligent systems.
2024 marks the research centre's fourth year of researching the next generation of deep learning methodology for extracting knowledge from complex image data. We look back at various innovation highlights achieved in the first half of Visual Intelligence's run.
Congratulations to Rwiddhi Chakraborty for successfully defending his PhD thesis and achieving the PhD degree in Science at UiT The Arctic University of Norway on December 13th 2024.
The graph community in Tromsø gathered on November 27th for the northernmost "Learning on Graph" Conference meetup.
Centre director Robert Jenssen recently gave a talk about the use and benefits of AI technologies during Pasientsikkerhetskonferansen 2024
Congratulations to Kristoffer Wickstrøm for receiving the Best Paper Award 2024 from the journal Pattern Recognition Letters!
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Professor Michael Kampffmeyer gave a presentation titled "Kunstig intelligens som forklarer hva den tenker" as part of a Norwegian Centre for E-Health Research Webinar (Norwegian dialogue).
I teorien ja, men i praksis fungerer det ikke slik.
EUGLOH students from partner institutions under the EUGLOH alliance can now apply for an exclusive mobility scholarship for the NLDL 2025 Winter School which covers travel, accommodation, and sustenance for successful applicants.
Visual Intelligence researchers represented the centre during Inspirasjonsdagen 2024. The event aimed to stimulate high school students' interest and curiosity for STEM and healthcare-related study programmes at UiT The Arctic University of Norway.