16 citations · 22 across the 5 of their papers we have counts for
6 papers
Neuralizer: General Neuroimage Analysis without Re-Training
Steffen Czolbe, Adrian V. Dalca
Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to…
Spot the Difference: Detection of Topological Changes via Geometric Alignment
Steffen Czolbe, Aasa Feragen, Oswin Krause
Geometric alignment appears in a variety of applications, ranging from domain adaptation, optimal transport, and normalizing flows in machine learning; optical flow and learned aug…
Semantic similarity metrics for learned image registration
Steffen Czolbe, Oswin Krause, Aasa Feragen
We propose a semantic similarity metric for image registration. Existing metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning intensity values, giving…
Is segmentation uncertainty useful?
Steffen Czolbe, Kasra Arnavaz, Oswin Krause +1
Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are des…
DeepSim: Semantic similarity metrics for learned image registration
Steffen Czolbe, Oswin Krause, Aasa Feragen
We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving…
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
Steffen Czolbe, Oswin Krause, Ingemar Cox +1
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function…