5 citations · 9 across the 3 of their papers we have counts for
6 papers · 1 filter
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
Ezequiel de la Rosa, David Robben, Diana M. Sima +2
Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core…
An augmentation strategy to mimic multi-scanner variability in MRI
Maria Ines Meyer, Ezequiel de la Rosa, Nuno Barros +3
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-c…
Unsupervised 3D Brain Anomaly Detection
Jaime Simarro, Ezequiel de la Rosa, Thijs Vande Vyvere +2
Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severit…
Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data
Mattias Billast, Maria Ines Meyer, Diana M. Sima +1
The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current…
Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors
Maria Ines Meyer, Ezequiel de la Rosa, Koen Van Leemput +1
With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorit…