M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation
Purpose: Deep learning-based medical image segmentation has achieved remarkable success, yet purely data-driven approaches often fail to exploit the rich mathematical structure inherent in medical images. We investigate whether explicit mathematical inductive biases, specifically matrix spectral analysis and vector calculus operators, can enhance segmentation beyond data-driven learning alone. Methods: We propose M-Net (Math-Augmented Network), which integrates three complementary mathematical priors into U-Net: (1) continuous spectral features derived from the condition number of centered local pixel matrices, providing a differentiable measure of texture ill-conditioning; (2) physical f...
| Authors | Jing Zhu, Ye Wang, Fumin Wang |
|---|---|
| Primary category | cs.CV |
| Published | 12 August 2026 |
| Last updated at source | 27 August 2026 |
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