PT Unknown
AU Debora Gil
   Antonio Esteban Lansaque
   Sebastian Stefaniga
   Mihail Gaianu
   Carles Sanchez
TI Data Augmentation from Sketch
BT International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
PY 2019
BP 155
EP 162
VL 11840
DE Data augmentation; cycleGANs; Multi-objective optimization
AB 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.
ER