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http://hdl.handle.net/10609/121466
Title: | Application of variational autoencoders in image-based analysis of cellular response profiles |
Author: | Muñoz Alloza, Jesús |
Tutor: | Reverter, Ferran Vegas Lozano, Esteban |
Others: | Sánchez-Pla, Alex |
Abstract: | Cell images reconstruction from a subset of the MCF7 image repository is the primary goal of this work. It is implemented through a variational autoencoder: a generative, unsupervised learning paradigm whose architecture consists of an encoder that reduces the dimensionality of the input space by obtaining a distribution over the latent space and a decoder, that rebuilds the inputs from the encoding. This work is completed with the description of the activities associated with the primary goal, like image segmentation and processing and infrastructure setup, the latest driven by automation tools and performed in a cloud environment. |
Keywords: | variational autoencoders deep learning generative modelling |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 22-Jun-2020 |
Publication license: | http://creativecommons.org/licenses/by-nc/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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jmunozalTFM0620memory.pdf | Memory of TFM | 8,66 MB | Adobe PDF | View/Open |
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