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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Acknowledgments
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia. Work supported by Spanish Projects FIS-G64384969, Generalitat de Catalunya, 2017-SGR-1624 and CERCA-Programme. D. Gil is a Serra Hunter fellow. The Titan X Pascal used for this research was donated by the NVIDIA Corporation.
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Gil, D., Esteban-Lansaque, A., Stefaniga, S., Gaianu, M., Sanchez, C. (2019). Data Augmentation from Sketch. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_16
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DOI: https://doi.org/10.1007/978-3-030-32689-0_16
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