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Found 7 papers, 7 papers with code
Neuralizer: General Neuroimage Analysis without Re-Training
We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning.
Spot the Difference: Detection of Topological Changes via Geometric Alignment
As a first step towards solving such alignment problems, we propose an unsupervised algorithm for the detection of changes in image topology.
Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
Is segmentation uncertainty useful?
Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem.
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
DeepSim: Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
