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The paper proposes a GAN-style architecture for translating medical images between modalities, using a Stackelberg-inspired leader-follower training scheme and a feature-distillation layer that uses target-domain information during training only. The main open question is whether the claimed reduction in anatomically inconsistent outputs holds up on clinical data and against strong baselines, since the abstract reports no quantitative results. Code is public, so readers can check the claims directly.
StaX-Net: Stackelberg Interleaved Asymmetric Network for Cross-Modality Medical Image Translation
arXiv:2610.34214v1 Announce Type: cross Abstract: Cross-modality medical image translation is challenged by anatomically inconsistent synthesis that can compromise clinical diagnosis. We propose StaX-Net, a Stackelberg interleaved asymmetric network that incorporates a partially coupled encoder-discriminator structure, and a 2D feature aggregation and spatial transformation (FAST) layer that distills target-supervised priors under the learning using privileged information paradigm. The shared blocks between the generator and discriminator enhance architectural feature alignment. To stabilize the leader-follower training, a Stackelberg-inspired asymmetric optimization scheme employs explicit shared-parameter freezing and asymmetric learning rates. The FAST layer distills target-domain priors during training and applies a source-only transform at inference. This constrains the synthesis process through target-informed feature representations relative to standard generative architectures. The code is available on GitHub for public use.
Abstract:Cross-modality medical image translation is challenged by anatomically inconsistent synthesis that can compromise clinical diagnosis. We propose StaX-Net, a Stackelberg interleaved asymmetric network that incorporates a partially coupled encoder-discriminator structure, and a 2D feature aggregation and spatial transformation (FAST) layer that distills target-supervised priors under the learning using privileged information paradigm. The shared blocks between the generator and discriminator enhance architectural feature alignment. To stabilize the leader-follower training, a Stackelberg-inspired asymmetric optimization scheme employs explicit shared-parameter freezing and asymmetric learning rates. The FAST layer distills target-domain priors during training and applies a source-only transform at inference. This constrains the synthesis process through target-informed feature representations relative to standard generative architectures. The code is available on GitHub for public use.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.34214 [cs.CV]
(or arXiv:2610.34214v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.34214 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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