ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation
<|reference_start|>ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation: 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.<|reference_end|>
@article{xu2024odc-sa,
    title={ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for
  Polyp Segmentation},
    author={Chenhao Xu and Yudian Zhang and Kaiye Xu and Haijiang Zhu},
    journal={Lecture Notes in Computer Science. vol 15044 (2025) 386-400},
    year={2024},
    doi={10.1007/978-981-97-8496-7_27},
    archivePrefix={arXiv},
    eprint={2405.06191},
    primaryClass={cs.CV}
}