Can Adversarial Networks Make Uninformative Colonoscopy Video Frames Clinically Informative?
This addresses a domain-specific issue for clinicians by potentially enhancing diagnostic accuracy in colonoscopy, though it appears incremental as it builds on existing adversarial methods.
The authors tackled the problem of artifacts in colonoscopy videos hindering colorectal cancer diagnosis by proposing an adversarial network framework to convert uninformative frames into clinically relevant ones, resulting in improved polyp detection performance with preliminary results.
Various artifacts, such as ghost colors, interlacing, and motion blur, hinder diagnosing colorectal cancer (CRC) from videos acquired during colonoscopy. The frames containing these artifacts are called uninformative frames and are present in large proportions in colonoscopy videos. To alleviate the impact of artifacts, we propose an adversarial network based framework to convert uninformative frames to clinically relevant frames. We examine the effectiveness of the proposed approach by evaluating the translated frames for polyp detection using YOLOv5. Preliminary results present improved detection performance along with elegant qualitative outcomes. We also examine the failure cases to determine the directions for future work.