Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection.

ABSTRACT: Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representation

SUBMITTER: Gan T 

PROVIDER: S-EPMC10709402 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature