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

Shuihua Wang, M. Emre Celebi, Yu Dong Zhang, Xiang Yu, Siyuan Lu, Xujing Yao, Qinghua Zhou, Martínez García Miguel, Yingli Tian*, Juan M. Gorriz, Ivan Tyukin

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

111 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)376-421
Number of pages46
JournalInformation Fusion
Volume76
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Data fusion
  • Data scarcity
  • High dimensionality
  • Missing data
  • Noise
  • Small dataset

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