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Highlights • A framework for automatically learning shape and appearance models without manual annotations.• Designed to run within a distributed privacy preserving framework.• When used as a pattern recognition approach,… Click to show full abstract
Highlights • A framework for automatically learning shape and appearance models without manual annotations.• Designed to run within a distributed privacy preserving framework.• When used as a pattern recognition approach, can give competitive classification accuracies for MNIST - particularly for small numbers of training examples.• Can handle missing data in the images.• Tested the model with 1900 brain scans and found that its latent variables can be used as features for pattern recognition.
Keywords:
learning shape;
models without;
shape appearance;
appearance models
Journal Title: Medical Image Analysis
Year Published: 2019
Link to full text (if available)
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Journal Title: Medical Image Analysis
Year Published: 2019
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!