Learning-based approaches for multimodal imaging

Stephan Winkler; Andreas Haghofer; Hannah Janout

Learning-based approaches for multimodal imaging

Stephan Winkler
, Andreas Haghofer
, Hannah Janout

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

In this chapter, we discuss the basics of machine learning and recent approaches that use deep neural network structures in the context of multimodality imaging. Furthermore, multimodality imaging often requires image registration to find the perfect alignment of two (or more) images, and image fusion. In this chapter, we describe machine learning-based approaches for both fields.

Original language	English
Title of host publication	Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2
Publisher	IOP Publishing Ltd.
Chapter	26
Pages	III.4.c-1 to III.4.c-11
Number of pages	11
Volume	2
Edition	1
ISBN (Electronic)	978-0-7503-3747-2
ISBN (Print)	978-0-7503-3745-8
Publication status	Published - May 2021

Cite this

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title = "Learning-based approaches for multimodal imaging",

abstract = "In this chapter, we discuss the basics of machine learning and recent approaches that use deep neural network structures in the context of multimodality imaging. Furthermore, multimodality imaging often requires image registration to find the perfect alignment of two (or more) images, and image fusion. In this chapter, we describe machine learning-based approaches for both fields.",

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AB - In this chapter, we discuss the basics of machine learning and recent approaches that use deep neural network structures in the context of multimodality imaging. Furthermore, multimodality imaging often requires image registration to find the perfect alignment of two (or more) images, and image fusion. In this chapter, we describe machine learning-based approaches for both fields.

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BT - Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2

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Learning-based approaches for multimodal imaging

Abstract

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