A CycleGAN variant with a depth-consistency loss translates simulated colonoscopy images to look real while preserving fold geometry, enabling synthetic-only training of a fold segmentation model that outperforms prior baselines on real data.
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Sim2Real in endoscopy segmentation with a novel structure aware image translation
A CycleGAN variant with a depth-consistency loss translates simulated colonoscopy images to look real while preserving fold geometry, enabling synthetic-only training of a fold segmentation model that outperforms prior baselines on real data.