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Authors: Stefan Röhrl 1 ; Lukas Bernhard 1 ; Manuel Lengl 1 ; Christian Klenk 2 ; Dominik Heim 2 ; Martin Knopp 1 ; 2 ; Simon Schumann 1 ; Oliver Hayden 2 and Klaus Diepold 2

Affiliations: 1 Chair of Data Processing, Technical University of Munich, Germany ; 2 Heinz-Nixdorf Chair of Biomedical Electronics, Technical University of Munich, Germany

Keyword(s): Quantitative Phase Imaging, Blood Cell Analysis, Machine Learning, Variational Autoencoder, Digital Holographic Microscopy, Microfluidics, Flow Cytometry.

Abstract: Digital holographic microscopy (DHM) has a high potential to be a new platform technology for medical diagnostics on a cellular level. The resulting quantitative phase images of label-free cells, however, are widely unfamiliar to the bio-medical community and lack in their degree of detail compared to conventionally stained microscope images. Currently, this problem is addressed using machine learning with opaque end-to-end models or inadequate handcrafted morphological features of the cells. In this work we present a modified version of the variational Autoencoder (VAE) to provide a more transparent and interpretable access to the quantitative phase representation of cells, their distribution and their classification. We can show a satisfying performance in the presented hematological use cases compared to classical VAEs or morphological features.

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Paper citation in several formats:
Röhrl, S.; Bernhard, L.; Lengl, M.; Klenk, C.; Heim, D.; Knopp, M.; Schumann, S.; Hayden, O. and Diepold, K. (2023). Explainable Feature Learning with Variational Autoencoders for Holographic Image Analysis. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 69-77. DOI: 10.5220/0011632800003414

@conference{bioimaging23,
author={Stefan Röhrl. and Lukas Bernhard. and Manuel Lengl. and Christian Klenk. and Dominik Heim. and Martin Knopp. and Simon Schumann. and Oliver Hayden. and Klaus Diepold.},
title={Explainable Feature Learning with Variational Autoencoders for Holographic Image Analysis},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING},
year={2023},
pages={69-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011632800003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING
TI - Explainable Feature Learning with Variational Autoencoders for Holographic Image Analysis
SN - 978-989-758-631-6
IS - 2184-4305
AU - Röhrl, S.
AU - Bernhard, L.
AU - Lengl, M.
AU - Klenk, C.
AU - Heim, D.
AU - Knopp, M.
AU - Schumann, S.
AU - Hayden, O.
AU - Diepold, K.
PY - 2023
SP - 69
EP - 77
DO - 10.5220/0011632800003414
PB - SciTePress

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