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arXiv · 2405.06191

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

Chenhao Xu·Yudian Zhang·Kaiye Xu·Haijiang Zhu
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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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Chenhao Xu, Yudian Zhang, Kaiye Xu, Haijiang Zhu. 2024-05-10. ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation. https://doi.org/10.1007/978-981-97-8496-7_27

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