Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment

Bachelor thesis (2021)

Authors

A. Korkić Electrical Engineering, Mathematics and Computer Science

Contributors

S. Makrodimitris Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
T.R.M. Abdelaal Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
M. Charrout Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
M.A.M.E. Eltager Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
E. Isufi (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Armin Korkić
Benchmarking VAE Cancer Hyperparameters Optimizer Learning Rate Latent Dimension Treatment Auto-Encoders
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Published Date

02-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algorithms we hope to improve personalized treatment for cancer patients. These machine learning algorithms are trying to learn a (latent) representation of the input. The problem is that this representation is hard to interpret and to observe the actual connections between the input and the output. That is why these algorithms are considered to behave like a black-box. In this research, a benchmark is conducted to measure how sensitive these algorithms are to changes in their hyper-parameters. The focus of this experiment are different types of variational auto encoders. We will measure how sensitive they are to changes in their: latent space dimension, learning rate and type of optimizer. The models will be trained on a dataset that contains the RNA gene-expression of different types of cancer tissues. To conclude that the optimizer may play the most important role performance wise for VAE models. Using the optimizer Adam and RMSprop results overall in lower reconstruction loss and overall in a more consistent performance.