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Authors: Jan Eric Lenssen 1 ; Anas Toma 1 ; Albert Seebold 1 ; Victoria Shpacovitch 2 ; Pascal Libuschewski 1 ; Frank Weichert 2 ; Jian-Jia Chen 1 and Roland Hergenröder 2

Affiliations: 1 TU Dortmund University, Germany ; 2 Leibniz-Institute for Analytical Science and ISAS e.V., Germany

ISBN: 978-989-758-279-0

Keyword(s): Nanoparticle Analysis, Deep Learning, Convolutional Neural Network, GPGPU Real Time Processing, Biosensing.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Real-Time Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In this work, we improve several steps of our PLASMON ASSISTED MICROSCOPY OF NANO-SIZED OBJECTS (PAMONO) sensor data processing pipeline through application of deep neural networks. The PAMONObiosensor is a mobile nanoparticle sensor utilizing SURFACE PLASMON RESONANCE (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low SIGNAL-TO-NOISE RATIO (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables our data processing pipeline to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop GRAPHICS PROCESSING UNITs (GPUs). (More)

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Paper citation in several formats:
Lenssen J., Toma A., Seebold A., Shpacovitch V., Libuschewski P., Weichert F., Chen J. and Hergenröder R. (2018). Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS, ISBN 978-989-758-279-0, pages 36-47. DOI: 10.5220/0006596400360047

@conference{biosignals18,
author={Jan Eric Lenssen and Anas Toma and Albert Seebold and Victoria Shpacovitch and Pascal Libuschewski and Frank Weichert and Jian-Jia Chen and Roland Hergenröder},
title={Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS,},
year={2018},
pages={36-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006596400360047},
isbn={978-989-758-279-0},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOSIGNALS,
TI - Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks
SN - 978-989-758-279-0
AU - Lenssen J.
AU - Toma A.
AU - Seebold A.
AU - Shpacovitch V.
AU - Libuschewski P.
AU - Weichert F.
AU - Chen J.
AU - Hergenröder R.
PY - 2018
SP - 36
EP - 47
DO - 10.5220/0006596400360047

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