  Polyp segmentation is of great importance in the early diagnosis and
treatment of colorectal cancer. Since polyps vary in their shape, size, color,
and texture, accurate polyp segmentation is very challenging. One promising way
to mitigate the diversity of polyps is to model the contextual relation for
each pixel such as using attention mechanism. However, previous methods only
focus on learning the dependencies between the position within an individual
image and ignore the contextual relation across different images. In this
paper, we propose Duplex Contextual Relation Network (DCRNet) to capture both
within-image and cross-image contextual relations. Specifically, we first
design Interior Contextual-Relation Module to estimate the similarity between
each position and all the positions within the same image. Then Exterior
Contextual-Relation Module is incorporated to estimate the similarity between
each position and the positions across different images. Based on the above two
types of similarity, the feature at one position can be further enhanced by the
contextual region embedding within and across images. To store the
characteristic region embedding from all the images, a memory bank is designed
and operates as a queue. Therefore, the proposed method can relate similar
features even though they come from different images. We evaluate the proposed
method on the EndoScene, Kvasir-SEG and the recently released large-scale
PICCOLO dataset. Experimental results show that the proposed DCRNet outperforms
the state-of-the-art methods in terms of the widely-used evaluation metrics.
