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Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects

journal contribution
posted on 13.09.2021, 10:39 by Shuihua Wang, M Emre Celebi, Yu-Dong Zhang, Xiang Yu, Siyuan Lu, Xujing Yao, Qinghua Zhou, Miguel Martinez-GarciaMiguel Martinez-Garcia, Yingli Tian, Juan M Gorriz, Ivan Tyukin
Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc., massive amounts of data are generated on a daily basis. While massive biomedical data sets yield more information about pathologies, they also present new challenges of how to fully explore the data. Data fusion methods are a step forward towards a better understanding of data by bringing multiple data observations together to increase the consistency of the information. However, data generation is merely the first step, and there are many other factors involved in the fusion process like noise, missing data, data scarcity, and high dimensionality. In this paper, an overview of the advances in data preprocessing in biomedical data fusion is provided, along with insights stemming from new developments in the field.

Funding

Royal Society International Exchanges Cost Share Award, UK (RP202G0230)

Medical Research Council Confidence in Concept Award, UK (MC_PC_17171)

Hope Foundation for Cancer Research, UK (RM60G0680)

Sino-UK Industrial Fund, UK (RP202G0289)

Global Challenges Research Fund (GCRF) UK (P202PF11)

United States National Science Foundation (1946391)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Information Fusion

Volume

76

Pages

376 - 421

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Information Fusion and the definitive published version is available at https://doi.org/10.1016/j.inffus.2021.07.001

Acceptance date

05/07/2021

Publication date

2021-07-10

Copyright date

2021

ISSN

1566-2535

eISSN

1872-6305

Language

en

Depositor

Dr Miguel Martinez Garcia. Deposit date: 7 September 2021