Please use this identifier to cite or link to this item:
http://hdl.handle.net/10609/127107
Title: | Generative Adversarial Networks Based Data Augmentation for Ultrasound Fetal Brain Planes Classification |
Author: | Montero Agudo, Alberto |
Tutor: | Burgos-Artizzu, Xavier Paolo Bonet-Carne, Elisenda |
Others: | Prados Carrasco, Ferran |
Abstract: | Generative adversarial networks have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This experimental case study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via generative adversarial networks and apply to ultrasound fetal brain plane classification tasks. State of the art Generative Adversarial Networks stylegan2-ada was applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that GAN-Based data augmentation combined with classical data augmentation outperforms classifiers with only classical data augmentation by 2% in both accuracy and area under the curve score. |
Keywords: | generative adversarial networks data augmentation ultrasound fetal brain images classification |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jan-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
amonteroagTFM0121memory.pdf | Memory of TFM | 3,97 MB | Adobe PDF | View/Open |
Share:
This item is licensed under a Creative Commons License