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Unsupervised Learning for Spherical Surface Registration
the UNC/UMN Baby Connectome Project Consortium
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
2
Citations (Scopus)
Overview
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Dive into the research topics of 'Unsupervised Learning for Spherical Surface Registration'. Together they form a unique fingerprint.
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Keyphrases
Anatomical Landmarks
25%
Computationally Intensive
25%
Cortical Surface
25%
Cortical Surface Registration
25%
Deformation Field
100%
Fast Learning
25%
Feature Similarity
25%
Learning Algorithm
25%
Neuroimaging Analysis
25%
Parametric Functions
50%
Registration Method
25%
Smoothness Constraint
25%
Spatial Normalization
25%
Spherical Convolutional Neural Networks
25%
Spherical Deformation
25%
Spherical Demons
25%
Spherical Surface
100%
Spherical Transform
25%
State-of-the-art Techniques
25%
Supervised Information
25%
Surface Registration
100%
Two-state
25%
U-Net
25%
Unsupervised Learning
100%
Well-defined
25%
Computer Science
Anatomical Landmark
25%
Convolutional Neural Network
25%
Deformation Field
100%
Good Performance
25%
Objective Function
25%
Parametric Function
50%
Supervised Information
25%
Surface Registration
100%
U-Net
25%
Unsupervised Learning
100%