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Found 7 papers, 1 papers with code
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.
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.
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.
