AI 中文总结
针对现有病理图像超分辨率技术易致形态平滑、语义漂移的问题，提出S³-Diff模型，通过SSA与SSFT实现高保真超分，性能优于现有最优方法，源代码将公开。
AI 中文摘要
数字病理学依赖高分辨率全切片图像实现准确诊断，但成像设备、存储及传输的限制常导致低分辨率病理图像在临床流程中更为常见。当前超分辨率技术常平滑诊断相关形态，导致纹理过度平滑与语义漂移，损害下游临床解读。为此，我们开发了结构语义协同扩散模型（S³-Diff），用于病理图像的高保真超分辨率。S³-Diff的核心是标本感知结构锚定（SSA），其结合固定SAM提取的预后感知组织支撑与低分辨率-高分辨率梯度差异，生成标本特异性结构锚定以保留病理形态。同时，我们引入结构引导语义保真度调优（SSFT），利用SSA衍生的结构监督适配DINOv3表示，将适配后的语义能量与低分辨率衍生的边缘及灰度线索结合，所得控制信号引导去噪以抑制随机伪影并维持结构一致性。大量实验结果表明，S³-Diff在重建质量与下游生存分析性能上均持续优于现有最优方法，源代码将公开。
英文摘要
Digital pathology relies on high-resolution whole slide images for accurate diagnosis, yet limitations in imaging devices, storage, and transmission often make lower-resolution pathology images more common in clinical workflows. Current super-resolution techniques often tend to smooth diagnostically relevant morphology, leading to over-smoothed textures and semantic drift that compromise downstream clinical interpretation. To this end, we develop the Structural Semantic Synergy Diffusion Model (S3-Diff), a diffusion framework for high-fidelity super-resolution of pathological images. The core of S3-Diff is Specimen-aware Structural Anchoring (SSA), which combines prognosis-aware tissue support extracted by a fixed SAM with LR-HR gradient discrepancies to generate a specimen-specific structural anchor to preserve pathological morphology. Concurrently, we introduce Structure-guided Semantic Fidelity Tuning (SSFT) to adapt DINOv3 representations using SSA-derived structural supervision. SSFT combines the adapted semantic energy with LR-derived edge and grayscale cues. The resulting control guides denoising to suppress stochastic artifacts and maintain structural consistency. Extensive experimental results demonstrate that S3-Diff consistently outperforms state-of-the-art methods in both reconstruction quality and downstream survival analysis performance. The source code will be made public.
