Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps K Wickstrøm, M Kampffmeyer, R Jenssen Medical Image Analysis 60, 101619, 2020 | 182* | 2020 |
Understanding convolutional neural networks with information theory: An initial exploration S Yu, K Wickstrøm, R Jenssen, JC Principe IEEE Transactions on Neural Networks and Learning Systems, 2020 | 103* | 2020 |
Mixing up contrastive learning: Self-supervised representation learning for time series K Wickstrøm, M Kampffmeyer, KØ Mikalsen, R Jenssen Pattern Recognition Letters 155, 54-61, 2022 | 77 | 2022 |
Auroral image classification with deep neural networks A Kvammen, K Wickstrøm, D McKay, N Partamies Journal of Geophysical Research: Space Physics 125 (10), e2020JA027808, 2020 | 38 | 2020 |
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks VN Nguyen, S Løkse, K Wickstrøm, M Kampffmeyer, D Roverso, ... European Conference on Computer Vision, 2020 | 38 | 2020 |
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy KK Wickstrøm, S Løkse, MC Kampffmeyer, S Yu, JC Príncipe, R Jenssen Entropy 25 (6), 899, 2023 | 34* | 2023 |
Uncertainty-aware deep ensembles for reliable and explainable predictions of clinical time series K Wickstrøm, KØ Mikalsen, M Kampffmeyer, A Revhaug, R Jenssen IEEE Journal of Biomedical and Health Informatics 25 (7), 2435-2444, 2020 | 34 | 2020 |
Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function S Kuttner, KK Wickstrøm, M Lubberink, A Tolf, J Burman, R Sundset, ... Journal of Cerebral Blood Flow & Metabolism 41 (9), 2229-2241, 2021 | 29 | 2021 |
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice S Kuttner, KK Wickstrøm, G Kalda, SE Dorraji, M Martin-Armas, A Oteiza, ... Biomedical Physics & Engineering Express 6 (1), 015020, 2020 | 20 | 2020 |
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus A Hedström, P Bommer, KK Wickstrøm, W Samek, S Lapuschkin, ... Transactions on Machine Learning Research, 2023 | 16 | 2023 |
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings DJ Trosten, R Chakraborty, S Løkse, KK Wickstrøm, R Jenssen, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 11 | 2023 |
Relax: Representation learning explainability KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, K Mikalsen, ... International Journal of Computer Vision, 1-27, 2023 | 10 | 2023 |
Machine learning detection of dust impact signals observed by the Solar Orbiter A Kvammen, K Wickstrøm, S Kociscak, J Vaverka, L Nouzak, A Zaslavsky, ... Annales Geophysicae 41 (1), 69-86, 2023 | 9 | 2023 |
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images KK Wickstrøm, EA Østmo, K Radiya, KØ Mikalsen, MC Kampffmeyer, ... Computerized Medical Imaging and Graphics 107, 102239, 2023 | 8 | 2023 |
Selective imputation for multivariate time series datasets with missing values A Blázquez-García, K Wickstrøm, S Yu, KØ Mikalsen, A Boubekki, ... IEEE Transactions on Knowledge and Data Engineering 35 (9), 9490-9501, 2023 | 7 | 2023 |
The Kernelized Taylor Diagram K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ... Nordic Artificial Intelligence Research and Development: 4th Symposium of …, 2023 | 3 | 2023 |
Explaining time series models using frequency masking T Brüsch, KK Wickstrøm, MN Schmidt, TS Alstrøm, R Jenssen arXiv preprint arXiv:2406.13584, 2024 | | 2024 |
View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images EA Østmo, KK Wickstrøm, K Radiya, MC Kampffmeyer, R Jenssen 2023 IEEE 33rd International Workshop on Machine Learning for Signal …, 2023 | | 2023 |
Advancing Deep Learning with Emphasis on Data-Driven Healthcare KK Wickstrøm UiT Norges arktiske universitet, 2022 | | 2022 |
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks BL Møller, C Igel, KK Wickstrøm, J Sporring, R Jenssen, B Ibragimov Forty-first International Conference on Machine Learning, 0 | | |