Structure-Preserving Diffusion Stylization for Medical Image Visualization and Diagnostic Enhancement
Keywords:
diffusion models; medical image visualization; structure preservation; diagnostic enhancement; clinical governance; robustness; fairness; sustainable infrastructureAbstract
Medical image visualization and diagnostic enhancement increasingly depend on generative models that can improve contrast, harmonize heterogeneous acquisition protocols, and render complex anatomy for expert interpretation. Diffusion stylization is attractive in this setting because it can synthesize high-fidelity appearance transformations while retaining fine spatial detail, yet clinical deployment imposes a stricter requirement than aesthetic plausibility: anatomical and pathological structures must remain faithful to the source evidence. This paper examines structure-preserving diffusion stylization as a socio-technical system rather than a purely model-centric technique. It analyzes how scale-separated conditioning, latent representations, segmentation priors, uncertainty signaling, and human-in-the-loop review can be combined to support visualization without allowing stylization to become diagnostic fabrication. The discussion covers architecture and trade-offs, diagnostic workflow integration, robustness under distribution shift, fairness across patient populations, governance and regulatory accountability, deployment infrastructure, and long-term sustainability. The central argument is that structure preservation must be designed as an enforceable property across data curation, model conditioning, verification, interface design, and institutional policy. When treated only as an objective during training, stylization risks producing compelling but unstable images that may mislead clinicians or exacerbate inequities. The paper concludes that clinically responsible diffusion stylization requires layered validation, transparent provenance, conservative defaults, and explicit separation between enhancement for perception and evidence for diagnosis.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.