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Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

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Version 2 2022-03-28, 13:47
Version 1 2021-11-09, 11:58
journal contribution
posted on 2022-03-28, 13:47 authored by S Wang, ME Celebi, YD Zhang, X Yu, S Lu, X Yao, Q Zhou, MG Miguel, Y Tian, JM Gorriz, I 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.

History

Citation

Information Fusion Volume 76, December 2021, Pages 376-421

Author affiliation

School of Informatics

Version

  • AM (Accepted Manuscript)

Published in

Information Fusion

Volume

76

Pagination

376 - 421

Publisher

Elsevier

issn

1566-2535

eissn

1872-6305

Acceptance date

2021-07-05

Copyright date

2021

Available date

2023-01-10

Language

eng