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

Milena Królikowska; Thomas Bocklitz

doi:10.1117/12.2651063

15 March 2023 Transfer learning for Raman spectroscopy in biological applications: A case study for bacterial classification (Conference Presentation)

Milena Królikowska, Thomas Bocklitz

Author Affiliations +

Proceedings Volume PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023; PC1239213 (2023) https://doi.org/10.1117/12.2651063
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

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.

Conference Presentation

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Milena Królikowska and Thomas Bocklitz "Transfer learning for Raman spectroscopy in biological applications: A case study for bacterial classification (Conference Presentation)", Proc. SPIE PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, PC1239213 (15 March 2023); https://doi.org/10.1117/12.2651063

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