  Automatic and accurate polyp segmentation plays an essential role in early
colorectal cancer diagnosis. However, it has always been a challenging task due
to 1) the diverse shape, size, brightness and other appearance characteristics
of polyps, 2) the tiny contrast between concealed polyps and their surrounding
regions. To address these problems, we propose a lesion-aware dynamic network
(LDNet) for polyp segmentation, which is a traditional u-shape encoder-decoder
structure incorporated with a dynamic kernel generation and updating scheme.
Specifically, the designed segmentation head is conditioned on the global
context features of the input image and iteratively updated by the extracted
lesion features according to polyp segmentation predictions. This simple but
effective scheme endows our model with powerful segmentation performance and
generalization capability. Besides, we utilize the extracted lesion
representation to enhance the feature contrast between the polyp and background
regions by a tailored lesion-aware cross-attention module (LCA), and design an
efficient self-attention module (ESA) to capture long-range context relations,
further improving the segmentation accuracy. Extensive experiments on four
public polyp benchmarks and our collected large-scale polyp dataset demonstrate
the superior performance of our method compared with other state-of-the-art
approaches. The source code is available at https://github.com/ReaFly/LDNet.
