Search Results for author:
Found 7 papers, 2 papers with code
GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease
Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data.
Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI
Lacunes of presumed vascular origin, also referred to as lacunar infarcts, are important to assess cerebral small vessel disease and cognitive diseases such as dementia.
Deep learning-based group-wise registration for longitudinal MRI analysis in glioma
The proposed methods may serve as an alternative to classical toolboxes, to provide further insight into glioma growth.
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis
Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice.
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data.
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space.
A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling
To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease.
