Search Results for author:
Found 4 papers, 3 papers with code
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D Medical Imaging Segmentation
Brain Lesion Segmentation From Mri
+3
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.
Transductive image segmentation: Self-training and effect of uncertainty estimation
It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization.
The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach
We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning.
