FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.17544v1
• Date: Fri, 23 May 2025 06:51:24 GMT
• Title: FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation
• Authors: Ruiqi Xing,
• Abstract summary: FreqU-FNet is a novel U-shaped segmentation architecture operating in the frequency domain.<n>Our framework incorporates a Frequency that leverages Low-Pass Convolution and Daubechies wavelet-based downsampling.<n>Experiments on multiple medical segmentation benchmarks demonstrate that FreqU-FNet consistently outperforms both CNN and Transformer baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical image segmentation faces persistent challenges due to severe class imbalance and the frequency-specific distribution of anatomical structures. Most conventional CNN-based methods operate in the spatial domain and struggle to capture minority class signals, often affected by frequency aliasing and limited spectral selectivity. Transformer-based models, while powerful in modeling global dependencies, tend to overlook critical local details necessary for fine-grained segmentation. To overcome these limitations, we propose FreqU-FNet, a novel U-shaped segmentation architecture operating in the frequency domain. Our framework incorporates a Frequency Encoder that leverages Low-Pass Frequency Convolution and Daubechies wavelet-based downsampling to extract multi-scale spectral features. To reconstruct fine spatial details, we introduce a Spatial Learnable Decoder (SLD) equipped with an adaptive multi-branch upsampling strategy. Furthermore, we design a frequency-aware loss (FAL) function to enhance minority class learning. Extensive experiments on multiple medical segmentation benchmarks demonstrate that FreqU-FNet consistently outperforms both CNN and Transformer baselines, particularly in handling under-represented classes, by effectively exploiting discriminative frequency bands.

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