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- W4308126795 startingPage "7448" @default.
- W4308126795 abstract "Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods." @default.
- W4308126795 created "2022-11-08" @default.
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- W4308126795 date "2022-11-02" @default.
- W4308126795 modified "2023-09-26" @default.
- W4308126795 title "A Review on Data Fusion of Multidimensional Medical and Biomedical Data" @default.
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- W4308126795 doi "https://doi.org/10.3390/molecules27217448" @default.
- W4308126795 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36364272" @default.
- W4308126795 hasPublicationYear "2022" @default.
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