Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
Por:
Montero A, Bonet E and Burgos-Artizzu XP
Publicada:
29 nov 2021
Ahead of Print:
29 nov 2021
Resumen:
Generative adversarial networks (GANs) 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 study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.
Filiaciones:
Montero A:
Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Bonet E:
Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clinic and Sant Joan de Deu), 08028 Barcelona, Spain
Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB), Universitat Politecnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain
Burgos-Artizzu XP:
Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
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