PMRNet: Physics-informed Multi-scale Refinement Network for Medical Image Segmentation

Medical image segmentation demands both high accuracy and computational efficiency, yet existing methods face a critical trade-off: CNNs lack global context while transformers incur prohibitive costs for deployment on resource-constrained devices. To address this challenge, we propose $P$hysics-informed $M$ulti-scale $R$efinement Network (PMRNet), integrating symplectic geometry, renormalization group theory, and entropy diffusion to guide feature learning. PMRNet features three innovations: (1) a physics-informed encoder with Enhanced Symplectic Convolution for boundary detection and Renormalization Group-informed Downsampling for information preservation; (2) a Pseudo-Global Receptive Field module achieving near-global context with linear complexity through entropy-driven diffusion; and (3) a boundary-aware decoder for precise delineation. With only $0.87$M parameters and $3.43$ GFLOPs, PMRNet achieves $87.25\%$ IoU and $92.56\%$ Dice on the challenging Clinic dataset, outperforming state-of-the-art (SOTA) models with even $100\times$ more parameters across $12$ medical imaging datasets while maintaining computational efficiency.