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A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Title data

Azam, Kazi Sultana Farhana ; Ryabchykov, Oleg ; Bocklitz, Thomas:
A Review on Data Fusion of Multidimensional Medical and Biomedical Data.
In: Molecules. Vol. 27 (2022) Issue 21 . - 7448.
ISSN 1420-3049
DOI: https://doi.org/10.3390/molecules27217448

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: data fusion; ultrasonography; single photon emission computed tomography; positron emission tomography; magnetic resonance imaging; computed tomography; Raman spectroscopy; MALDI imaging; mammography; fluorescence lifetime imaging microscopy; deep learning; machine learning
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 31 May 2023 12:28
Last Modified: 31 May 2023 12:28
URI: https://eref.uni-bayreuth.de/id/eprint/81072