# Stackelberg-Inspired Asymmetric Optimization

Cross-modality medical image translation has been troubled by anatomically inconsistent synthesis, and standard generative adversarial networks update generator and discriminator together, letting opposing gradients pull on shared representations. StaX-Net interleaves multi-scale computational blocks between the generator and discriminator, lets the discriminator update first while shared blocks are frozen during the generator step, and uses target MRI only during training to guide a feature transform. On the Abdomen/Pelvis, Head and Neck, and Thorax subsets of SynthRAD 2025 it is compared with five baselines, reaching an Abdomen/Pelvis VGG perceptual distance of 642.1, below Pix2Pix at 991.2 and UNet-AE at 1687.9.

Cross-modality medical image translation has been troubled by anatomically inconsistent synthesis, and standard generative adversarial networks update generator and discriminator together, letting opposing gradients pull on shared representations. StaX-Net interleaves multi-scale computational blocks between the generator and discriminator, lets the discriminator update first while shared blocks are frozen during the generator step, and uses target MRI only during training to guide a feature transform. On the Abdomen/Pelvis, Head and Neck, and Thorax subsets of SynthRAD 2025 it is compared with five baselines, reaching an Abdomen/Pelvis VGG perceptual distance of 642.1, below Pix2Pix at 991.2 and UNet-AE at 1687.9.