Kristoffer Wickstrøm
Job description
Jeg er førsteamanuensis i Maskinlæringsgruppa ved UiT Norges arktiske universitet og forskningsleder for tolkbarhet i SFI Visual Intelligence. Mitt hovedforskningsområde er dyp læring, med særlig fokus på forklarbarhet og læring med begrensede data. Jeg er også interessert i usikkerhetsmodellering, informasjonsteori, kjernemetoder og medisinsk bildeanalyse. Jeg har tidligere vært gjesteforsker i bildebehandlingslaboratoriet ved Universitetet i Valencia med professor Gustau Camps-Valls og i Understandable Machine Intelligence Laboratory ved Technical University of Berlin med professor Marina M.-C. Höhne.
Jing Wang,
Songhe Feng,
Kristoffer Wickstrøm,
Michael Kampffmeyer
:
AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering
Computer Vision and Pattern Recognition 2025 DOI
Duy Khoi Tran,
Van Nhan Nguyen,
Kristoffer Wickstrøm,
Michael Kampffmeyer
:
WOODWORK: A deep-learning based framework for woodpecker damage detection in powerline inspection
International Journal of Electrical Power & Energy Systems 01. Oktober 2025 DOI
Solveig Thrun,
Stine Hansen,
Zijun Sun,
Nele Blum,
Suaiba A. Salahuddin,
Kristoffer Wickstrøm
et al.:
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Lecture Notes in Computer Science (LNCS) 20. September 2025 DOI
Kristoffer Wickstrøm,
Hedström, Anna,
Marina Marie-Claire Höhne
:
From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation | SpringerLink
Lecture Notes in Computer Science (LNCS) 2025 DOI
Kristoffer Wickstrøm,
Thea Brüsch,
Michael Kampffmeyer,
Robert Jenssen
:
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
| Proceedings of the AAAI Conference on Artificial Intelligence
Proceedings of the AAAI Conference on Artificial Intelligence 11. April 2025 DOI
Lars Uebbing,
Harald Lykke Joakimsen,
Luigi Tommaso Luppino,
Iver Martinsen,
Andrew McDonald,
Kristoffer Wickstrøm
et al.:
Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
Proceedings of Machine Learning Research (PMLR) 2025
Suaiba Amina Salahuddin,
Elisabeth Wetzer,
Kristoffer Wickstrøm,
Solveig Thrun,
Michael Kampffmeyer,
Robert Jenssen
:
Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance
Lecture Notes in Computer Science (LNCS) 16. Juni 2025 DOI
Bjørn Møller,
Christian Igel,
Kristoffer Knutsen Wickstrøm,
Jon Sporring,
Robert Jenssen,
Bulat Ibragimov
:
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
Kristoffer Wickstrøm,
Daniel Johansen Trosten,
Sigurd Eivindson Løkse,
Ahcene Boubekki,
Karl Øyvind Mikalsen,
Michael Kampffmeyer
et al.:
RELAX: Representation Learning Explainability
Ane Blazquez-Garcia,
Kristoffer Knutsen Wickstrøm,
Shujian Yu,
Karl Øyvind Mikalsen,
Ahcene Boubekki,
Angel Conde
et al.:
Selective Imputation for Multivariate Time Series Datasets with Missing Values
Eirik Agnalt Østmo,
Kristoffer Wickstrøm,
Keyur Radiya,
Michael Kampffmeyer,
Robert Jenssen
:
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Daniel Johansen Trosten,
Rwiddhi Chakraborty,
Sigurd Eivindson Løkse,
Kristoffer Wickstrøm,
Robert Jenssen,
Michael Kampffmeyer
:
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Kristoffer Wickstrøm,
Eirik Agnalt Østmo,
Keyur Radiya,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Robert Jenssen
:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Kristoffer Knutsen Wickstrøm,
Marina Marie-Claire Höhne
:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Transactions on Machine Learning Research (TMLR) 2023 ARKIV
Kristoffer Wickstrøm,
Sigurd Eivindson Løkse,
Michael Kampffmeyer,
Shujian Yu,
José C. Príncipe,
Robert Jenssen
:
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
Kristoffer Wickstrøm,
Michael Kampffmeyer,
Karl Øyvind Mikalsen,
Robert Jenssen
:
Mixing up contrastive learning: Self-supervised representation learning for time series
Christian Salomonsen,
Kristoffer Wickstrøm,
Samuel Kuttner,
Elisabeth Wetzer
:
Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images
27. September 2025
Lars Uebbing,
Harald Lykke Joakimsen,
Kristoffer Wickstrøm,
Michael Kampffmeyer,
Sebastien Francois Lefevre,
Arnt Børre Salberg
et al.:
NOFE - Neural Operator Function Embedding
2025
Lars Uebbing,
Harald Lykke Joakimsen,
Kristoffer Wickstrøm,
Michael Kampffmeyer,
Sebastien Francois Lefevre,
Arnt Børre Salberg
et al.:
NOFE Neural Operator Function Embedding
2025 DOI
Solveig Thrun,
Stine Hansen,
Zijun Sun,
Nele Blum,
Suaiba Amina Salahuddin,
Kristoffer Wickstrøm
et al.:
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
20. September 2025
Simen Strømme,
Kristoffer Wickstrøm,
Kristoffer Søvik,
Geir Lippestad,
Johannes Bergh
:
Politiske partier har lansert KI-chatboter
13. Mai 2025 DOI
Elisabeth Wetzer,
Youssef Wally,
Artem Galushko,
Elisavet Kozyri,
Kristoffer Wickstrøm
:
How to Tackle Bias and Protect Privacy in the Age of AI?
17. September 2025
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-Informed Machine Learning for dynamic PET modeling
2025
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-informed deep learning for predicting the arterial input function in dynamic PET imaging
2025
Suaiba Amina Salahuddin,
Elisabeth Wetzer,
Kristoffer Wickstrøm,
Solveig Thrun,
Michael Kampffmeyer,
Robert Jenssen
:
Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance
2025
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-informed deep learning for improved input function estimation in motion-blurred dynamic [18F]FDG PET images
2025
Petter Bjørklund,
Kristoffer Knutsen Wickstrøm,
Keyur Radiya
:
Finner leverkreft med kunstig intelligens
uit.no 2024
Robert Jenssen,
Kristoffer Knutsen Wickstrøm,
Petter Bjørklund
:
Slik kan kunstig intelligens hjelpe legene
Forskning.no 2024
Kristoffer Knutsen Wickstrøm
:
Kunstig intelligens fra beslutningstøtte til beslutningstaker – hvem er ansvarlig når ting går galt?
2024
Kristoffer Knutsen Wickstrøm
:
Forskningsfronten på kunstig intelligens
2024
Lars Uebbing,
Harald Lykke Joakimsen,
Luigi Tommaso Luppino,
Iver Martinsen,
Andrew McDonald,
Kristoffer Knutsen Wickstrøm
et al.:
Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
2024
Kristoffer Knutsen Wickstrøm
:
Muligheter og utfordringer for kunstig intelligens i næringslivet
2024
Petter Bjørklund,
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
KI har superkrefter som kan hjelpe legene våre
uit.no 2024 FULLTEKST
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Kristoffer Knutsen Wickstrøm
:
Morrasendinga fra NRK i Troms - Forskere ved UiT Norges arktiske universitet utvikler kunstig intelligens til å finne leverkreft.
10. Juni 2024
Kristoffer Knutsen Wickstrøm
:
Facebook skal merke KI-bilder: – Stor nyhet
07. Februar 2024
Kristoffer Knutsen Wickstrøm
:
Kunstig Intelligens, utfordringer og muligheter for næringslivet
2024
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
Ja takk til «krysskulturelle» prosjekter drevet fram av teknologiutvikling
2023
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
Hvordan bør fotavtrykket av regjeringens satsing på kunstig intelligens se ut i Nord-Norge i 2030?
2023
Kristoffer Wickstrøm
:
The aggregation method matters in faithfulness evaluation of XAI
2023
Andreas Kvammen,
Kristoffer Wickstrøm,
Samuel Kociscak,
Jakub Vaverka,
Libor Nouzák,
Arnaud Zaslavsky
et al.:
Machine learning detection of dust impact signals
2023
Kristoffer Knutsen Wickstrøm
:
XAI for time series analysis
2023
Kristoffer Knutsen Wickstrøm
:
XAI for understanding of SSL representations
2023
Kristoffer Knutsen Wickstrøm,
Daniel Johansen Trosten,
Sigurd Eivindson Løkse,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Robert Jenssen
:
RELAX: Representation Learning Explainability
2022
Daniel Johansen Trosten,
Kristoffer Wickstrøm,
Shujian Yu,
Sigurd Eivindson Løkse,
Robert Jenssen,
Michael Kampffmeyer
:
Deep Clustering with the Cauchy-Schwarz Divergence
2022
Kristoffer Wickstrøm
:
The past, present, and future of XAI
2022
Kristoffer Wickstrøm,
Jenssen Robert
:
Advancing Deep Learning with Emphasis on Data-Driven Healthcare
UiT Norges arktiske universitet 28. Oktober 2022
Samuel Kuttner,
Luigi Tommaso Luppino,
Kristoffer Wickstrøm,
Nils Thomas Doherty Midtbø,
Seyed Esmaeil Dorraji,
Ana Oteiza
et al.:
Deep learning derived input function in dynamic 18F-FDG PET imaging of mice
2022
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Research interests
Dyp læring.
Tolkbar kunstig intelligens.
Usikkerhetsanalyse.
Medisinsk bildeanalyse.
Læring med begrenset data.