Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging
This work addresses the need for better polyp detection in colorectal cancer screening when specialized NBI equipment is unavailable, representing an incremental improvement by applying an existing method to a new domain.
The paper tackled the problem of improving polyp detection in colonoscopy by converting white-light imaging (WLI) to synthetic narrow-band imaging (SNBI) using a CycleGAN-based framework, resulting in enhanced detection performance on the generated SNBI images compared to original WLI.
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.