773 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Norwegian University of Science and TechnologyNO13 papers
- SINTEFNO5 papers
- Oslo University HospitalNO3 papers
- University Medical Center GroningenNL2 papers
- University of OsloNO2 papers
- Amsterdam University Medical CentersNL1 paper
- Bjerknes Centre for Climate ResearchNO1 paper
- Bristol Royal Hospital for ChildrenGB1 paper
- Centre de Recherche en Acquisition et Traitement de l'Image pour la SantéFR1 paper
- Centre Hospitalier Universitaire de Saint-ÉtienneFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Dutch Cancer SocietyNL1 paper
16 papers
FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation using Multi-modal Images
Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1
Medical image segmentation plays a crucial role in AI-assisted diagnostics, surgical planning, and treatment monitoring. Accurate and robust segmentation models are essential for e…
Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Ashkan Moradi, Fadila Zerka, Joeran S. Bosma +7
Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (…
The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications
Giovanni di Sarra, Barbara Bravi, Yasser Roudi
Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural arch…
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen +2
Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in hi…
A novel imaging setup for hybrid radiotherapy tailored PET/MR in patients with head and neck cancer
R. M. Winter, O. Engelsen, O. J. Bratting +4
Purpose: Radiotherapy commonly relies on CT, but there is growing interest in using hybrid PET/MR. Therefore, dedicated hardware setups have been proposed for PET/MR systems which…
Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures
Felix Tempel, Espen Alexander F. Ihlen, Lars Adde +1
In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP)…
Cardiac valve event timing in echocardiography using deep learning and triplane recordings
Benjamin Strandli Fermann, John Nyberg, Espen W. Remme +9
Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of e…
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
Javier Pérez de Frutos, André Pedersen, Egidijus Pelanis +5
Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Differe…
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen +40
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative s…
Preoperative brain tumor imaging: models and software for segmentation and standardized reporting
D. Bouget, A. Pedersen, A. S. Jakola +27
For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the l…
Boolean function metrics can assist modelers to check and choose logical rules
John Zobolas, Pedro T. Monteiro, Martin Kuiper +1
Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-ba…
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
David Bouget, André Pedersen, Johanna Vanel +2
As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strat…
Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture
David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey +3
Automatic and consistent meningioma segmentation in T1-weighted MRI volumes and corresponding volumetric assessment is of use for diagnosis, treatment planning, and tumor growth ev…
Towards human-level performance on automatic pose estimation of infant spontaneous movements
Daniel Groos, Lars Adde, Ragnhild Støen +2
Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorde…
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Sarah Leclerc, Erik Smistad, João Pedrosa +11
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has be…
Magnetic Lenz lenses increase the limit-of-detection in nuclear magnetic resonance
Nils Spengler, Peter T. While, Markus V. Meissner +2
A high NMR detection sensitivity is indispensable when dealing with mass and volume-limited samples, or whenever a high spatial resolution is required. The use of miniaturised RF c…