SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic
Shape Alignment
- URL: http://arxiv.org/abs/2104.13449v1
- Date: Tue, 27 Apr 2021 19:49:46 GMT
- Title: SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic
Shape Alignment
- Authors: Elvis Nunez, Andrew Lizarraga, and Shantanu H. Joshi
- Abstract summary: SrvfNet is a generative deep learning framework for the joint multiple alignment of large collections of functional data.
Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data.
We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.
- Score: 6.404122934568859
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We present SrvfNet, a generative deep learning framework for the joint
multiple alignment of large collections of functional data comprising
square-root velocity functions (SRVF) to their templates. Our proposed
framework is fully unsupervised and is capable of aligning to a predefined
template as well as jointly predicting an optimal template from data while
simultaneously achieving alignment. Our network is constructed as a generative
encoder-decoder architecture comprising fully-connected layers capable of
producing a distribution space of the warping functions. We demonstrate the
strength of our framework by validating it on synthetic data as well as
diffusion profiles from magnetic resonance imaging (MRI) data.
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