Data Augmentation from Sketch
Gil, Debora (Centre de Visió per Computador (Bellaterra, Catalunya))
Esteban Lansaque, Antonio (Centre de Visió per Computador (Bellaterra, Catalunya))
Stefaniga, Sebastian (West University of Timisoara)
Gaianu, Mihail (West University of Timisoara)
Sánchez Ramos, Carles (Centre de Visió per Computador (Bellaterra, Catalunya))
Universitat Autònoma de Barcelona

Imprint:	Cham, Switzerland: Springer, 2019
Description:	8 pag.
Abstract:	State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content. We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
Grants:	European Commission 712949 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1624 Ministerio de Economía, Industria y Competitividad FIS-G64384969
Rights:	Tots els drets reservats.
Language:	Anglès
Series:	Lecture Notes in Computer Science book series (LNCS) ; 11840
Document:	Capítol de llibre ; Versió acceptada per publicar
Subject:	Data augmentation ; CycleGANs ; Multi-objective optimization
Published in:	Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures, 2019, p. 155-162, ISBN 978-3-030-32689-0

DOI: 10.1007/978-3-030-32689-0_16

Postprint 9 p, 5.7 MB

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