188 citations · 393 across the 26 of their papers we have counts for
28 papers
Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin +5
Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benefit from both temporal breast…
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen +5
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains un…
Longitudinal Multi-View Breast Cancer Risk Prediction
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin +5
Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have s…
ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm +2
Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations t…
NOFE - Neural Operator Function Embedding
Lars Uebbing, Harald L. Joakimsen, Siyan Chen +6
Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processes. To bridge this gap, we in…
Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Training
Kristoffer Wickstrøm, Teresa Dorszewski, Siyan Chen +3
Current approaches for designing self-explainable models (SEMs) require complicated training procedures and specific architectures which makes them impractical. With the advance of…
The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment
Solveig Thrun, Stine Hansen, Zijun Sun +8
Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can be personalized, especially for h…
Fast Voxel-Wise Kinetic Modeling in Dynamic PET using a Physics-Informed CycleGAN
Christian Salomonsen, Samuel Kuttner, Michael Kampffmeyer +4
Tracer kinetic modeling serves a vital role in diagnosis, treatment planning, tracer development and oncology, but burdens practitioners with complex and invasive arterial input fu…
Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [F]FDG PET Images
Christian Salomonsen, Kristoffer K. Wickstrøm, Samuel Kuttner +1
Kinetic modeling enables \textit{in vivo} quantification of tracer uptake and glucose metabolism in [F]Fluorodeoxyglucose ([F]FDG) dynamic positron emission tomog…
Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +3
Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable…
Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
Suaiba Amina Salahuddin, Teresa Dorszewski, Marit Almenning Martiniussen +7
Understanding what deep learning (DL) models learn is essential for the safe deployment of artificial intelligence (AI) in clinical settings. While previous work has focused on pix…
In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging
Valentina Corbetta, Floris Six Dijkstra, Regina Beets-Tan +3
Deep learning models in medical imaging often achieve strong in-distribution performance but struggle to generalise under distribution shifts, frequently relying on spurious correl…
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal FDG PET imaging
Christian Salomonsen, Luigi T Luppino, Fredrik Aspheim +7
Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accurate kinetic modeling requires pre…
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Solveig Thrun, Stine Hansen, Zijun Sun +7
Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for hi…
From Colors to Classes: Emergence of Concepts in Vision Transformers
Teresa Dorszewski, Lenka Tětková, Robert Jenssen +2
Vision Transformers (ViTs) are increasingly utilized in various computer vision tasks due to their powerful representation capabilities. However, it remains understudied how ViTs p…
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer +1
Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly import…
FLEXtime: Filterbank learning to explain time series
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt +2
State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are diff…
FreqRISE: Explaining time series using frequency masking
Thea Brüsch, Kristoffer Knutsen Wickstrøm, Mikkel N. Schmidt +2
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision…
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +2
Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in m…
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse +3
Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are pron…
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya +3
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they a…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen +1
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing…
RELAX: Representation Learning Explainability
Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse +4
Despite the significant improvements that representation learning via self-supervision has led to when learning from unlabeled data, no methods exist that explain what influences t…