Generative Adversarial Networks Based Data Augmentation for Ultrasound Fetal Brain Planes Classification
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 t...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/127107 |
| Acceso en línea: | https://hdl.handle.net/10609/127107 |
| Access Level: | acceso abierto |
| Palabra clave: | generative adversarial networks data augmentation ultrasound fetal brain images classification xarxes contradictòries generatives augment de dades classificació d'imatges cerebrals fetals per ultrasons redes generativas adversarias aumento de datos clasificación de imágenes de ultrasonido del cerebro fetal Databases -- TFM Bases de dades -- TFM Bases de datos -- TFM |
| Sumario: | 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. |
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