Scientific publications

At Visual Intelligence we work across our innovation areas to extract knowledge from large volumes of visual data more efficiently through automatic and intelligent data analysis. The work to address the core research challenges in deep learning: working with limited training data, utilizing context and dependencies, providing explainability, confidence and uncertainty, are important in all the innovation areas.

Featured blog posts

Principle of Relevant Information for Graph Sparsification

May 20, 2022
By
Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen and Jose C. Principe

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

February 14, 2022
By
Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

All publications

Principle of Relevant Information for Graph Sparsification

By authors:

Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen and Jose C. Principe

Published in:

Conference on Uncertainty in Artificial Intelligence (UAI) 2022

on

May 20, 2022

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

on

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

on

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

on

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

on

February 21, 2022

A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

By authors:

Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

Published in:

Cancers 2022, 14, 2974

on

February 14, 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

on

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

on

February 11, 2022

Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation

By authors:

Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen and Michael Kampffmeyer

Published in:

IEEE International Symposium on Biomedical Imaging (ISBI) 2022

on

February 1, 2022

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

By authors:

Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang

Published in:

Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)

on

December 23, 2021

Other publications

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