dammak, mohamed badreddine (2020) Generative adversarial networks as data augmentation in Medical imaging PFE - Project Graduation, ENSTA.



Deep learning models allowed the achievement of many breakthroughs in computer vision. Armed with convolutional network, models can classify images, locate objects and generate synthetic images. Training efficient networks requires a large amount of data which is a challenge for medical images. In addition malignant cases represent a fractions of collected data. In this work, we study the application of Generative Adversarial network on mammography and tomography patches. We generate high fidelity labeled images using Progressive Growing GANs at 512x512 resolution. However, they were not beneficial when used for training. PGGAN training set contained 26,000 patches. We also training a cross modality image-to-image translation models that transforms annotated mammography patches to tomography patches. The synthetic patches are realistic and capture the malignancy of the source image. By fine tuning a CNN classification network on generated tomography patches we managed improved its performance.

Item Type:Thesis (PFE - Project Graduation)
Subjects:Information and Communication Sciences and Technologies
Mathematics and Applications
ID Code:8290
Deposited By:Bader Dammak
Deposited On:09 nov. 2020 13:26
Dernière modification:09 nov. 2020 13:26

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