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.

翻译：为应对结直肠癌（CRC）日益增长的患病率，针对息肉检测与切除的筛查项目已被证实具有显著效用。结肠镜检查被认为是结直肠癌筛查中性能最佳的检查手段。为简化检查流程，基于深度学习的白光成像（WLI）自动息肉检测方法已得到开发。相较于白光成像，窄带成像（NBI）可在结肠镜检查中提升息肉分类效果，但需特殊设备支持。本文提出一种基于CycleGAN的框架，将常规白光成像图像转换为合成窄带成像（SNBI），作为在无法获取窄带成像时提升白光成像目标检测性能的预处理方法。本文首先证实，与相对相似的白光成像数据集相比，窄带成像可获得更优的息肉检测结果。其次，实验结果表明，相较于原始白光成像，我们提出的模态转换方法能在由白光成像生成的合成窄带成像图像上实现更优的息肉检测效果。这是因为我们的白光成像到合成窄带成像转换模型能够增强生成图像中息肉表面模式的观测能力。