FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation

Jiashu Xu

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arXiv · 2411.18975

FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation

Jiashu Xu

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Abstract

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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Jiashu Xu. 2024-11-28. FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation. https://arxiv.org/abs/2411.18975

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