A Norwegian centre for research-based innovation

Through long-term research in close collaboration between industry, public institutions and prominent research partners, we enable novel innovations, technology transfer, internationalization and researcher training.

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We research the next generation of deep learning methodology for visual data and produce solutions for our consortium partners across innovation areas in medicine and health, marine science, energy, and earth observation.

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VI seminar #23 – nnU-Net

May 12, 2022

July 4, 2022
July 5, 2022
UiT Teknologibygget auditorium 1.022 and Online

Master of Science Per Roald Leikanger will hold his trial lecture on the announced topic on July 4 2022 at 14:00 and on 5 July at 14:00 publically defend his thesis for the PhD degree in Nautical Operations.

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Using Machine Learning to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

June 21, 2022

Developing artificial intelligence methods to help pathologists in analysis of whole slide images for cancer treatment and detection.

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Principle of Relevant Information for Graph Sparsification

May 20, 2022

How can we remove the redundant or less-informative edges in a graph without changing its main structural properties?

Recent publications

Mitral Annulus Segmentation and Anatomical Orientation Detection in TEE Images Using Periodic 3D CNN

By authors:

Børge Solli Andreassen, David Völgyes, Eigil Samset, Anne H. Schistad Solberg

Published in:

IEEE Access, Engineering in Medicine and Biology Section


May 10, 2022

Toward Scalable and Unified Example-Based Explanation and Outlier Detection

By authors:

Penny Chong, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

Published in:

IEEE Transactions on Image Processing, vol. 31, pp. 525-540, 2022


March 11, 2022

M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining

By authors:

Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, Xiaodan Liang

Published in:

Conference on Computer Vision and Pattern Recognition (CVPR), 2022


March 3, 2022

Data-Driven Robust Control Using Reinforcement Learning

By authors:

Phuong D. Ngo, Miguel Tejedor and Fred Godtliebsen

Published in:

Appl. Sci. 2022, 12(4), 2262


February 21, 2022

Mixing up contrastive learning: Self-supervised representation learning for time series

By authors:

Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen

Published in:

Pattern Recognition Letters, Volume 155, March 2022, Pages 54-61


February 12, 2022

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

By authors:

Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer

Published in:

Medical Image Analysis


February 11, 2022

Research challenges

Visual Intelligence address the research challenges of deep learning and computer vision that limit our user partners in utilizing their complex visual data in their applications.

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Innovation areas

We contribute to reliable use of AI to detect heart disease, monitor the environment and potential natural disasters as well as detecting natural resources. Read more about our work in the different innovation areas.

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Our partners

Visual Intelligence is a consortium headed by UiT The Arctic University of Norway with research partners at the University of Oslo and the Norwegian Computing Center. Together with our consortium of high-profile user partners, we create cutting-edge solutions that will be implemented in the applications of the user partners.

UiT The Arctic University of Norway logoUiO: University of Oslo logoNorwegian Computing Centre logoUniversity hospital of north norway logoHelse nord ikt logoInstitute of marine research logoTerratec logo
Kongsberg satellite services logoGE Healthcare logoEquinor logoCancer Registry of Norwat logo