Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells
Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from th...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
8 June 2019
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| In: |
International journal of computer assisted radiology and surgery
Year: 2019, Jahrgang: 14, Heft: 11, Pages: 1847-1857 |
| ISSN: | 1861-6429 |
| DOI: | 10.1007/s11548-019-02010-3 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1007/s11548-019-02010-3
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| Verfasserangaben: | Christian Ritter, Thomas Wollmann, Patrick Bernhard, Manuel Gunkel, Delia M. Braun, Ji-Young Lee, Jan Meiners, Ronald Simon, Guido Sauter, Holger Erfle, Karsten Rippe, Ralf Bartenschlager, Karl Rohr |
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| 520 | |a Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied. | ||
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