| 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 |
| Doi |
10.1109/IPTA.2017.8310145 |
