用傅里叶分析增强U-Net，更好分割医学影像中的长程依赖和周期性结构
FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation
- 引入傅里叶分析层，融合自注意力机制捕捉全局与局部特征
- 在多个医学图像数据集上表现优于基准模型，平衡了性能与复杂度
- 适合需要精确分割病灶或结构的医学影像任务，如肿瘤识别
医学图像分割是现代医学研究与临床实践的关键环节。尽管卷积神经网络（CNN）在此领域表现优异，但其固有缺陷在于难以捕捉图像中的长程依赖关系。而变压器（Transformers）虽擅长建模全局上下文，却常难以有效捕获局部特征。为此，本文提出FAN-UNet，一种结合基于傅里叶分析网络（FAN）视觉主干与U-Net架构的新模型，有效解决生物医学图像分割中长程依赖与周期性建模的挑战。所提出的视觉-FAN层融合了FAN层与自注意力机制，利用傅里叶分析使模型能够有效捕捉长程依赖与周期性关系。在多个医学影像数据集上的大量实验表明，FAN-UNet在模型复杂度与性能之间实现了良好平衡，验证了其在医学图像分割任务中的有效性与实用性。
原文摘要 · Abstract (English)
Medical image segmentation is a critical aspect of modern medical research and clinical practice. Despite the remarkable performance of Convolutional Neural Networks (CNNs) in this domain, they inherently struggle to capture long-range dependencies within images. Transformers, on the other hand, are naturally adept at modeling global context but often face challenges in capturing local features effectively. Therefore, we presents FAN-UNet, a novel architecture that combines the strengths of Fourier Analysis Network (FAN)-based vision backbones and the U-Net architecture, effectively addressing the challenges of long-range dependency and periodicity modeling in biomedical image segmentation tasks. The proposed Vision-FAN layer integrates the FAN layer and self-attention mechanisms, leveraging Fourier analysis to enable the model to effectively capture both long-range dependencies and periodic relationships. Extensive experiments on various medical imaging datasets demonstrate that FAN-UNet achieves a favorable balance between model complexity and performance, validating its effectiveness and practicality for medical image segmentation tasks.
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