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Found 6 papers, 0 papers with code
Learning Deep Features for Shape Correspondence with Domain Invariance
Further, unsupervised learning is demonstrated to learn complex anatomy features using the supervised domain adaptation from features learned on simpler anatomy.
GENs: Generative Encoding Networks
Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a {\em generative encoding network}.
Unsupervised Shape Normality Metric for Severity Quantification
The severity of an anatomical deformity often serves as a determinant in the clinical management of patients.
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
We propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration.
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Statistical shape modeling is an important tool to characterize variation in anatomical morphology.
Clustering With Pairwise Relationships: A Generative Approach
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense.
