Kristoffer Wickstrøm

Orcid: 0000-0003-1395-7154

According to our database1, Kristoffer Wickstrøm authored at least 29 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [<sup>18</sup>F]FDG PET imaging.
CoRR, July, 2025

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction.
CoRR, June, 2025

From Colors to Classes: Emergence of Concepts in Vision Transformers.
CoRR, March, 2025

Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance.
Proceedings of the Image Analysis - 23rd Scandinavian Conference, 2025

Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting.
Proceedings of the Northern Lights Deep Learning Conference, 2025

NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks.
Proceedings of the Northern Lights Deep Learning Conference, 2025

FreqRISE: Explaining time series using frequency masking.
Proceedings of the Northern Lights Deep Learning Conference, 2025

AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
FLEXtime: Filterbank learning for explaining time series.
CoRR, 2024

Explaining time series models using frequency masking.
CoRR, 2024

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation.
Proceedings of the Computer Vision - ECCV 2024 Workshops, 2024

2023
Selective Imputation for Multivariate Time Series Datasets With Missing Values.
IEEE Trans. Knowl. Data Eng., September, 2023

RELAX: Representation Learning Explainability.
Int. J. Comput. Vis., June, 2023

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy.
Entropy, June, 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.
Trans. Mach. Learn. Res., 2023

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images.
Comput. Medical Imaging Graph., 2023

View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Mixing up contrastive learning: Self-supervised representation learning for time series.
Pattern Recognit. Lett., 2022

The Kernelized Taylor Diagram.
Proceedings of the Nordic Artificial Intelligence Research and Development, 2022

2021
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration.
IEEE Trans. Neural Networks Learn. Syst., 2021

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series.
IEEE J. Biomed. Health Informatics, 2021

RELAX: Representation Learning Explainability.
CoRR, 2021

2020
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps.
Medical Image Anal., 2020

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels.
CoRR, 2019

2018
Uncertainty Modeling and interpretability in Convolutional Neural Networks for Polyp Segmentation.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018


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