A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography

ConclusionThe 3D  Glow-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.
Source: International Journal of Computer Assisted Radiology and Surgery - Category: Intensive Care Source Type: research