Search Results for author:
Found 8 papers, 5 papers with code
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
A Lightweight Causal Model for Interpretable Subject-level Prediction
Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's diagnosis, from medical images.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features
In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas.
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images
Survival prediction models can potentially be used to guide treatment of glioblastoma patients.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
Semi-Supervised Variational Autoencoder for Survival Prediction
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks.
