Zusammenfassung
Segmentation of cell nuclei is essential for analyzing highcontent histological screens. Often, parameters of automatic approaches need to be optimized, which is tedious and difficult to perform manually. We propose a novel hyperparameter optimization framework, which formulates optimization as a combination of candidate sampling and an optimization strategy. We present a clustering based and a deep neural network based pipeline for nuclei segmentation, for which the parameters are optimized using state of the art optimizers as well as a novel optimizer. The pipelines were applied to challenging prostate cancer tissue images. We performed a quantitative evaluation using 28,388 parameter settings. It turned out that the deep neural network outperforms the clustering based pipeline, while the results for different optimizers vary slightly.
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Literatur
Wang Y, Du S, Balakrishnan S, et al. Stochastic Zeroth-order Optimization in High Dimensions. arXiv:1710.10551; 2017.
Ramani S, Blu T, Unser M. Monte-carlo SURE: a black-box optimization of regularization parameters for general denoising algorithms. IEEE Trans Image Process. 2008;17(9):1540-1554.
Hansen N, Auger A, Ros R, et al.; ACM. Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. Proc GECCO. 2010; p. 1689-1696.
Teodoro G, Kurҫ TM, Taveira LF, et al. Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines. Bioinformatics. 2016;33(7):1064-1072.
Snoek J, Larochelle H, Adams RP. Practical bayesian optimization of machine learning algorithms. Proc Adv Neural Inf Process Syst. 2012; p. 2951-2959.
Bergstra J, Yamins D, Cox DD; Citeseer. Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. Proc SciPy. 2013; p. 13-20.
Komer B, Bergstra J, Eliasmith C. Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. Proc ICML Workshop AutoML. 2014; p. 2825-2830.
Golovin D, Solnik B, Moitra S, et al.; ACM. Google vizier: a service for black-box optimization. Proc SIGKDD. 2017; p. 1487-1495.
Ronneberger O, Fischer P, Brox T; Springer. U-net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234-241.
Hutter F, Hoos HH, Leyton-Brown K; Springer. Sequential model-based optimization for general algorithm configuration. Proc LION. 2011; p. 507-523.
Chen T, Guestrin C; ACM. Xgboost: a scalable tree boosting system. Proc SIGKDD. 2016; p. 785-794.
Goldberg DE. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley; 1989.
Parzen E. On estimation of a probability density function and mode. Ann Math Stat. 1962;33(3):1065-1076.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Wollmann, T. et al. (2019). Black-Box Hyperparameter Optimization for Nuclei Segmentation in Prostate Tissue Images. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_75
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DOI: https://doi.org/10.1007/978-3-658-25326-4_75
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