  Detecting and segmenting polyps is crucial for expediting the diagnosis of
colon cancer. This is a challenging task due to the large variations of polyps
in color, texture, and lighting conditions, along with subtle differences
between the polyp and its surrounding area. Recently, vision Transformers have
shown robust abilities in modeling global context for polyp segmentation.
However, they face two major limitations: the inability to learn local
relations among multi-level layers and inadequate feature aggregation in the
decoder. To address these issues, we propose a novel decoder architecture aimed
at hierarchically aggregating locally enhanced multi-level dense features.
Specifically, we introduce a novel module named Dense Attention Gate (DAG),
which adaptively fuses all previous layers' features to establish local feature
relations among all layers. Furthermore, we propose a novel nested decoder
architecture that hierarchically aggregates decoder features, thereby enhancing
semantic features. We incorporate our novel dense decoder with the PVT backbone
network and conduct evaluations on five polyp segmentation datasets: Kvasir,
CVC-300, CVC-ColonDB, CVC-ClinicDB, and ETIS. Our experiments and comparisons
with nine competing segmentation models demonstrate that the proposed
architecture achieves state-of-the-art performance and outperforms the previous
models on four datasets. The source code is available at:
https://github.com/krushi1992/Dense-Decoder.
