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Brain MRI super-resolution using generative adversarial networks

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hdl:2117/126234

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Sánchez, Irina
Vilaplana Besler, VerónicaMés informacióMés informacióMés informació
Document typeConference lecture
Defense date2018
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the up sampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution.
CitationSánchez, I., Vilaplana, V. Brain MRI super-resolution using generative adversarial networks. A: International conference on Medical Imaging with Deep Learning. "International conference on Medical Imaging with Deep Learning: Amsterdam, 4 - 6th July 2018". 2018, p. 1-8. 
URIhttp://hdl.handle.net/2117/126234
Publisher versionhttps://midl.amsterdam/scientific-program/
Other identifiershttps://openreview.net/group?id=MIDL.amsterdam/2018/Conference#oral-papers
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