188 citations · 393 across the 26 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen
Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motiv…