FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation
This is an incremental improvement for biomedical image segmentation, addressing specific challenges in medical imaging tasks.
The paper tackles the problem of capturing long-range dependencies and periodic relationships in biomedical image segmentation by proposing FAN-UNet, which combines Fourier Analysis Network backbones with U-Net, achieving a favorable balance between model complexity and performance.
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.