RTA-Former: Reverse Transformer Attention for Polyp Segmentation
Zhikai Li Murong Yi Ali Uneri Sihan Niu Craig Jones
Polyp segmentation is a key aspect of colorectal cancer prevention, enabling
early detection and guiding subsequent treatments. Intelligent diagnostic
tools, including deep learning solutions, are widely explored to streamline and
potentially automate this process. However, even with many powerful network
architectures, there still comes the problem of producing accurate edge
segmentation. In this paper, we introduce a novel network, namely RTA-Former,
that employs a transformer model as the encoder backbone and innovatively
adapts Reverse Attention (RA) with a transformer stage in the decoder for
enhanced edge segmentation. The results of the experiments illustrate that
RTA-Former achieves state-of-the-art (SOTA) performance in five polyp
segmentation datasets. The strong capability of RTA-Former holds promise in
improving the accuracy of Transformer-based polyp segmentation, potentially
leading to better clinical decisions and patient outcomes. Our code will be
publicly available on GitHub.