116 citations · 237 across the 34 of their papers we have counts for
9 papers · 1 filter
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles
Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim
Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However…
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
Rob A. J. de Mooij, Josien P. W. Pluim, Cian M. Scannell
Self-supervised pretraining (SSP) has shown promising results in learning from large unlabeled datasets and, thus, could be useful for automated cardiovascular magnetic resonance (…
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Rudolf L. M. van Herten, Ioannis Lagogiannis, Jelmer M. Wolterink +8
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large train…
Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification
Hassan Keshvarikhojasteh, Marc Aubreville, Christof A. Bertram +2
Machine learning models have become integral to many fields, but their reliability, defined as producing dependable, trustworthy, and domain-consistent predictions, remains a criti…
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab +6
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic…
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim +1
The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification u…