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Siedhoff/etal/2017a: Unsupervised Data Analysis for Virus Detection with a Surface Plasmon Resonance Sensor

Bibtype Inproceedings
Bibkey Siedhoff/etal/2017a
Author Siedhoff, Dominic and Strauch, Martin and Shpacovitch, Victoria and Merhof, Dorit
Title Unsupervised Data Analysis for Virus Detection with a Surface Plasmon Resonance Sensor
Booktitle International Conference on Image Processing Theory, Tools and Applications (IPTA 2017)
Abstract We propose an unsupervised approach for virus detection with a biosensor based on surface plasmon resonance. A column-based non-negative matrix factorisation (NNCX) serves to select virus candidate time series from the spatio-temporal data. The candidates are then separated into true virus adhesions and false positive NNCX responses by fitting a constrained virus model function. In the evaluation on ground truth data, our unsupervised approach compares favourably to a previously published supervised approach that requires more parameters.
Year 2017
Projekt SFB876-B2
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