PVT2DNet: Polyp segmentation with vision transformer and dual decoder refinement strategy

Abstract

Colorectal carcinoma is a prevalent malignancy worldwide. Accurate polyp segmentation, along with endoscopic resection, can significantly reduce its incidence and mortality. Most polyp segmentation neural networks are CNN-based and single decoder strategy architectures, which learn limited robust representations. In this paper, we propose a novel network with the vision transformer and dual decoder refinement strategy called PVT2DNet to overcome some limitations of current networks and achieve more precise automated polyp segmentation. The PVT2DNet adopts a pyramid vision transformer encoder and enhances the multi-level features with the context-enhanced module (CEM). Moreover, instead of directly feeding features into a single decoder, we introduce a dual partial cascaded decoder refinement strategy to excavate more informative polyp cues. Extensive experimentations on five widely adopted datasets demonstrate the proposed network outperforms other state-of-the-art on most metrics.