Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band
  Imaging
Mathias Ramm Haugland Hemin Ali Qadir Ilangko Balasingham
To cope with the growing prevalence of colorectal cancer (CRC), screening
programs for polyp detection and removal have proven their usefulness.
Colonoscopy is considered the best-performing procedure for CRC screening. To
ease the examination, deep learning based methods for automatic polyp detection
have been developed for conventional white-light imaging (WLI). Compared with
WLI, narrow-band imaging (NBI) can improve polyp classification during
colonoscopy but requires special equipment. We propose a CycleGAN-based
framework to convert images captured with regular WLI to synthetic NBI (SNBI)
as a pre-processing method for improving object detection on WLI when NBI is
unavailable. This paper first shows that better results for polyp detection can
be achieved on NBI compared to a relatively similar dataset of WLI. Secondly,
experimental results demonstrate that our proposed modality translation can
achieve improved polyp detection on SNBI images generated from WLI compared to
the original WLI. This is because our WLI-to-SNBI translation model can enhance
the observation of polyp surface patterns in the generated SNBI images.