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Found 6 papers, 0 papers with code
Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
Automated surface segmentation is important and challenging in many medical image analysis applications.
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.
Unsupervised anomaly localization using VAE and beta-VAE
An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss.
Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields
To the best of our knowledge, this is the first study to apply a 3-D neural network with a CRFs model for direct surface segmentation.
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
Robust Image Segmentation Quality Assessment
Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications.
