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Azam, Kazi Sultana Farhana ; Ryabchykov, Oleg ; Bocklitz, Thomas:
A Review on Data Fusion of Multidimensional Medical and Biomedical Data.
In: Molecules.
Bd. 27
(2022)
Heft 21
.
- 7448.
ISSN 1420-3049
DOI: https://doi.org/10.3390/molecules27217448
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.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| 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 |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 31 Mai 2023 12:28 |
| Letzte Änderung: | 31 Mai 2023 12:28 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/81072 |