https://www.selleckchem.com/pr....oducts/gsk-lsd1-2hcl
When trained with data augmentation by 3DGlow, the 3DDenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods. The 3DGlow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography. T
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