Paper_v30.pdf (3.4 MB)
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
journal contribution
posted on 2021-09-13, 10:39 authored 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 TyukinDue 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 FusionVolume
76Pages
376 - 421Publisher
Elsevier BVVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher 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.2021.07.001Acceptance date
2021-07-05Publication date
2021-07-10Copyright date
2021ISSN
1566-2535eISSN
1872-6305Publisher version
Language
- en
Depositor
Dr Miguel Martinez Garcia. Deposit date: 7 September 2021Usage metrics
Keywords
Data fusionNoiseData scarcityHigh dimensionalityMissing dataSmall datasetScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsComputer ScienceMEDICAL IMAGE REGISTRATIONPARTIAL LEAST-SQUARESHANDLING MISSING DATACONVOLUTIONAL NEURAL-NETWORKPRINCIPAL COMPONENT ANALYSISACTIVE CONTOUR MODELSMR-TRUS FUSIONMULTIPLE IMPUTATIONALZHEIMERS-DISEASEANISOTROPIC DIFFUSIONArtificial Intelligence & Image ProcessingArtificial Intelligence and Image Processing
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