Search Results for author:
Found 10 papers, 2 papers with code
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants.
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net.
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations.
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1. 5T MRI).
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.
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.
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.
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.
