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Transfer learning for Raman spectroscopy in biological applications : A case study for bacterial classification (Conference Presentation)

Title data

Królikowska, Milena ; Bocklitz, Thomas:
Transfer learning for Raman spectroscopy in biological applications : A case study for bacterial classification (Conference Presentation).
In: Advanced Chemical Microscopy for Life Science and Translational Medicine 2023. - Bellingham, Wash. : SPIE , 2023 . - PC1239213
DOI: https://doi.org/10.1117/12.2651063

Abstract in another language

Raman spectroscopy is a label-free, non-invasive spectroscopic technique, which can be utilized for many biomedical and diagnostic investigations. To do so, chemometric modelling strategies are used, but they lead to a low generalizability of the models. To tackle this issue we investigated transfer learning (TL) approaches for deep learning (DL) based modelling of Raman spectra for classification of three bacterial spore species. In initial test we found that TL can facilitate the usage of DL for time-consuming measurement modalities, because it can help to deal with low dataset sizes.

Further data

Item Type: Article in a book
Refereed: Yes
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 13:05
Last Modified: 31 May 2023 13:05
URI: https://eref.uni-bayreuth.de/id/eprint/81078