Title	Convnets, a different view of approximating diffeomorphisms in medical image registration
Creator	Kuang, Dongyang
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-12-11T17:14
Description	As with the heat of artificial intelligence, there are more and more researches starting to investigate the possible geometric transformations using data-driven methods such as convolutional neural networks. In this talk, I will start by introducing some existing work that learn 2D linear transformations in an unsupervised way. This then will be followed by an overview of some recent works focusing on nonlinear transformations in 3D volumetric data. Finally, I will present results from the joint work with my supervisor using our network architecture called FAIM.
Extent	42.0
Subject	Mathematics; Calculus of variations and optimal control; optimization; Differential geometry
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Ottawa
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-06-10
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0379372
URI	http://hdl.handle.net/2429/70588
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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