679 citations
- Erasmus MCNL24 papers
- Delft University of TechnologyNL9 papers
- Amsterdam University Medical CentersNL5 papers
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- Eindhoven University of TechnologyNL3 papers
- Leiden University Medical CenterNL3 papers
- Radboud University NijmegenNL3 papers
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8 papers · 1 filter
Robust Alignment of the Human Embryo in 3D Ultrasound using PCA and an Ensemble of Heuristic, Atlas-based and Learning-based Classifiers Evaluated on the Rotterdam Periconceptional Cohort
Nikolai Herrmann, Marcella C. Zijta, Stefan Klein +5
Standardized alignment of the embryo in three-dimensional (3D) ultrasound images aids prenatal growth monitoring by facilitating standard plane detection, improving visualization o…
Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
Kaouther Mouheb, Marawan Elbatel, Janne Papma +8
While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmar…
CLAIRE-DSA: Fluoroscopic Image Classification for Quality Assurance of Computer Vision Pipelines in Acute Ischemic Stroke
Cristo J. van den Berg, Frank G. te Nijenhuis, Mirre J. Blaauboer +9
Computer vision models can be used to assist during mechanical thrombectomy (MT) for acute ischemic stroke (AIS), but poor image quality often degrades performance. This work prese…
Evaluating the Fairness of Neural Collapse in Medical Image Classification
Kaouther Mouheb, Marawan Elbatel, Stefan Klein +1
Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders its clinical applicability in equ…
qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model
Shishuai Wang, Hua Ma, Juan A. Hernandez-Tamames +2
Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstr…
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
Bo Li, Wiro J. Niessen, Stefan Klein +4
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this me…