Search Results for author:
Found 9 papers, 3 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
An augmentation strategy to mimic multi-scanner variability in MRI
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-center and multi-scanner data.
Unsupervised 3D Brain Anomaly Detection
Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt).
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data
A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.
Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors
In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors.
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.
