Seeing Beyond Pixels: Improving Medical Image Segmentation with Frequency Data
![Phi-SegNet employs bi-feature mask formers and attention-guided skip connections to integrate encoder features, then refines segmentation through phase supervision and reverse Fourier attention [latex] \mathcal{R}\mathcal{F} [/latex] modules-a spectral filtering approach designed to sharpen boundary localization despite the inevitable complexities of production deployment.](https://arxiv.org/html/2601.16064v1/Figures/total_architecture.jpg)
A new deep learning framework, Phi-SegNet, boosts the accuracy of medical image analysis by incorporating often-overlooked phase information from the frequency domain.
Science
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