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- Title:
Autoencoder Features for Differentiation of Leukocytes based on Digital Holographic Microscopy (DHM)
- Document type:
- Konferenzbeitrag
- Contribution type:
- Vortrag / Präsentation
- Author(s):
- Stefan Röhrl, Matthias Ugele, Christian Klenk, Dominik Heim, Oliver Hayden and Klaus Diepold
- Abstract:
- The differentiation and counting of leukocytes is essential for the diagnosis of leukemia. This work investigates the suitability of Deep Convolutional Autoencoders and Principal Component Analysis (PCA) to generate robust features from the 3D image data of a digital holographic microscope (DHM). The results show that the feature space is not trivially separable in both cases. A terminal classification by a Support Vector Machine (SVM) favors the uncorrelated PCA features.
- Keywords:
- Blood Cell Analysis, Autoencoder, Convolutional Neural Networks, Digital Holographic Microscopy, Phase Images
- Dewey Decimal Classification:
- 620 Ingenieurwissenschaften
- Editor:
- Roberto Moreno-Díaz, Franz R. Pichler, Alexis Quesada-Arencibia
- Book / Congress title:
- Computer Aided Systems Theory - EUROCAST 2019
- Publisher:
- Springer
- Year:
- 2019
- Year / month:
- 2019-02
- Month:
- Feb
- Pages:
- 8
- WWW:
- https://link.springer.com/chapter/10.1007/978-3-030-45096-0_35
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