S$^3$-Diff: Structural Semantic Synergy Diffusion Model for High Fidelity Super Resolution of Pathological Images

2026-08-04

AI Summary

This work addresses the limitations of existing pathological image super-resolution methods, which often compromise diagnostically critical morphological details due to over-smoothing or semantic distortion. To mitigate these issues, the authors propose S³-Diff, a novel diffusion model that integrates specimen-aware structural anchoring and structure-guided semantic fidelity tuning to jointly preserve tissue architecture and semantic consistency. The approach leverages multiple priors—including SAM-derived tissue masks, LR-HR gradient discrepancies, DINOv2 semantic features, and edge and intensity cues—within a unified diffusion framework to enable coordinated structural and semantic control. Experimental results demonstrate that S³-Diff significantly outperforms state-of-the-art methods in both reconstruction quality and downstream survival analysis tasks, effectively retaining morphology relevant to clinical diagnosis.

Abstract

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.

Problem

• super-resolution
• pathological images
• semantic drift
• morphology preservation
• digital pathology

Innovation

• Structural Semantic Synergy
• Specimen-aware Structural Anchoring
• Structure-guided Semantic Fidelity Tuning
• Pathological Image Super-Resolution
• Diffusion Model