

\section{Conclusion}

We presented Simplex-Aligned Diffusion, a generative classifier that resolves the geometric conflict between Gaussian noise and discrete simplices by operating on the unbounded logit manifold. Integrated with a Cross-Granularity Interaction module, our framework achieves superior noise robustness and calibration on APTOS2019 and HAM10000 while maintaining competitive accuracy. 

Crucially, the improved calibration directly enhances clinical decision reliability, as the model's confidence scores serve as a trustworthy proxy for diagnostic accuracy, thereby reducing the risk of overconfident misdiagnosis. Our qualitative analysis further reveals that the model maintains a "meaningful failure" mode under extreme noise; even in cases of incorrect classification, the framework preserves persistent visual localization of pathological regions, whereas baselines often lose semantic focus.

While our current validation focuses on 2D benchmarks, the Simplex-Aligned formulation is {inherently modality-agnostic}. Future work will explore its application to 3D medical volumes (e.g., CT/MRI)~\cite{xie2026refining} and 1D signals, alongside acceleration techniques like consistency distillation and flow matching to facilitate real-time clinical deployment.