Core Concepts
Proposing a novel decoder architecture for polyp segmentation using Dense Attention Gate and hierarchical feature aggregation.
Stats
Recently, vision Transformers have shown robust abilities in modeling global context for polyp segmentation.
The proposed architecture achieves state-of-the-art performance and outperforms previous models on four datasets.
Quotes
"Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer."
"Various U-shaped models have demonstrated remarkable performance gains in polyp segmentation."