Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation
<|reference_start|>Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation: In medical images, various types of lesions often manifest significant differences in their shape and texture. Accurate medical image segmentation demands deep learning models with robust capabilities in multi-scale and boundary feature learning. However, previous networks still have limitations in addressing the above issues. Firstly, previous networks simultaneously fuse multi-level features or employ deep supervision to enhance multi-scale learning. However, this may lead to feature redundancy and excessive computational overhead, which is not conducive to network training and clinical deployment. Secondly, the majority of medical image segmentation networks exclusively learn features in the spatial domain, disregarding the abundant global information in the frequency domain. This results in a bias towards low-frequency components, neglecting crucial high-frequency information. To address these problems, we introduce SF-UNet, a spatial-frequency dual-domain attention network. It comprises two main components: the Multi-scale Progressive Channel Attention (MPCA) block, which progressively extract multi-scale features across adjacent encoder layers, and the lightweight Frequency-Spatial Attention (FSA) block, with only 0.05M parameters, enabling concurrent learning of texture and boundary features from both spatial and frequency domains. We validate the effectiveness of the proposed SF-UNet on three public datasets. Experimental results show that compared to previous state-of-the-art (SOTA) medical image segmentation networks, SF-UNet achieves the best performance, and achieves up to 9.4\% and 10.78\% improvement in DSC and IOU. Codes will be released at https://github.com/nkicsl/SF-UNet.<|reference_end|>
@article{zhou2024spatial-frequency,
    title={Spatial-Frequency Dual Progressive Attention Network For Medical Image
  Segmentation},
    author={Zhenhuan Zhou, Along He, Yanlin Wu, Rui Yao, Xueshuo Xie and Tao Li},
    journal={arXiv preprint arXiv:2406.07952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07952},
    primaryClass={eess.IV cs.CV}
}