Attention augmented residual autoencoder for efficient polyp segmentation

Abstract

Colon cancer has been reported to be one of the frequently diagnosed cancers and the leading cause of cancer deaths. Early detection and removal of malicious polyps, which are precursors of colon cancer, can enormously lessen the fatality rate. The detection and segmentation of polyps in colonoscopy is a challenging task even for an experienced colonoscopist, due to divergences in the size, shape, texture, and the close resemblance of polyps with the colon lining. Machine-assisted detection, localization, and segmentation of polyps in the screening procedure can profoundly help the clinicians. Autoencoder-based architectures used in polyp segmentation lack the efficiency in incorporating both local and long-range pixel dependencies. To address the challenges in the automatic segmentation of colon polyps we propose an autoencoder architecture, augmented with a feature attention module in the decoder part. The salient features from RGB colonoscopic images are extracted using the residual skip-connected autoencoder. The decoder attention module joins spatial subspace with feature subspace extracted from the deep residual convolutional neural network and enhances the feature weight for precise segmentation of polyp regions. Extensive experiments on four publicly available polyp datasets demonstrate that the proposed architecture provides very impressive performance in terms of segmentation metrics (Dice scores and Jaccard scores) when compared with the state-of-the-art polyp segmentation approaches.