Record |
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 |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
11840 |
Issue |
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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. |
Address |
Shenzhen; China; October 2019 |
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CLIP |
Notes |
IAM; 600.145; 601.337; 600.139; 600.145 |
Approved |
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Call Number |
Admin @ si @ GES2019 |
Serial |
3359 |
Permanent link to this record |