Search Results for author:
Found 14 papers, 4 papers with code
AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography
This hinders the serial evaluation of blood flow, and often causes undesired vasculature and contrast flow alterations, leading to impeded usability in clinical practice.
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.
CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography
Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA.
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.
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles.
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.
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.
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
Hydranet: Data Augmentation for Regression Neural Networks
The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.
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.
