FAN-Unet：利用视觉傅里叶分析块增强Unet以用于生物医学图像分割
FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation
AI总结：
针对医学图像分割中CNN难捕捉长程依赖、Transformer难有效捕捉局部特征的问题，提出结合FAN视觉骨干与U-Net的FAN-UNet架构，通过Vision-FAN层整合傅里叶分析与自注意力，在多数据集上验证了其在复杂度与性能间的良好平衡。
摘要：
医学图像分割是现代医学研究和临床实践的关键方面。尽管卷积神经网络（CNNs）在该领域表现出色，但它们本质上难以捕捉图像内的长程依赖关系。另一方面，Transformer天生擅长建模全局上下文，但往往难以有效捕捉局部特征。因此，我们提出了FAN-UNet，这是一种结合了基于傅里叶分析网络（FAN）的视觉骨干和U-Net架构优势的新型架构，有效解决了生物医学图像分割任务中长程依赖和周期性建模的挑战。所提出的Vision-FAN层整合了FAN层和自注意力机制，利用傅里叶分析使模型能够有效捕捉长程依赖关系和周期性关系。在各种医学成像数据集上的大量实验表明，FAN-UNet在模型复杂度和性能之间取得了良好的平衡，验证了其在医学图像分割任务中的有效性和实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
