ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation

• URL: http://arxiv.org/abs/2405.06191v1
• Date: Fri, 10 May 2024 02:13:32 GMT
• Title: ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation
• Authors: Chenhao Xu, Yudian Zhang, Kaiye Xu, Haijiang Zhu,
• Abstract summary: We design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The ODC block can extract multi-directional features using transposed rectangular convolution kernels. The Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions.
• Score: 0.624976855972012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate polyp segmentation is crucial for the early detection and prevention of colorectal cancer. However, the existing polyp detection methods sometimes ignore multi-directional features and drastic changes in scale. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming an orthogonal feature vector basis, which solves the issue of random feature direction changes and reduces computational load. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention Module (ERA) is used to re-combinane effective features, and Structures of Shallow Reverse Attention Mechanism (SRA) is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.

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