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- W3089742212 abstract "Data-driven methods usually require a large amount of labelled data for training and generalization, especially in medical imaging. Targeting the colonoscopy field, we develop the Optical Flow Generative Adversarial Network (OfGAN) to transform simulated colonoscopy videos into realistic ones while preserving annotation. The advantages of our method are three-fold: the transformed videos are visually much more realistic; the annotation, such as optical flow of the source video is preserved in the transformed video, and it is robust to noise. The model uses a cycle-consistent structure and optical flow for both spatial and temporal consistency via adversarial training. We demonstrate that the performance of our OfGAN overwhelms the baseline method in relative tasks through both qualitative and quantitative evaluation." @default.
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- W3089742212 date "2020-01-01" @default.
- W3089742212 modified "2023-10-15" @default.
- W3089742212 title "OfGAN: Realistic Rendition of Synthetic Colonoscopy Videos" @default.
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- W3089742212 doi "https://doi.org/10.1007/978-3-030-59716-0_70" @default.
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