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Author
Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez
Title
Data Augmentation from Sketch
Type
Conference Article
Year
2019
Publication
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Abbreviated Journal
Volume
11840
Issue
Pages
155-162
Keywords
Data augmentation; cycleGANs; Multi-objective optimization
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.
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Shenzhen; China; October 2019
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Notes
IAM; 600.145; 601.337; 600.139; 600.145
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no
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Admin @ si @ GES2019
Serial
3359
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