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

• URL: http://arxiv.org/abs/2608.03540v1
• Date: Tue, 04 Aug 2026 12:16:36 GMT
• Title: S$^3$-Diff: Structural Semantic Synergy Diffusion Model for High Fidelity Super Resolution of Pathological Images
• Abstract summary: 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 interpretation.<n>We develop the Structural Semantic Diffusion Synergy Model (S3-Diff), a diffusion framework for high-fidelity super-resolution of pathological images.<n>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.
• Score: 26.861049165066635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Morphology-Aware Implicit Super-Resolution Network for Pathological Images [27.177894894882673]
  Morph-ISR is a morphology-aware implicit super-resolution framework for Digital Pathology.<n>It restores diagnostically relevant details with sub-pixel precision under complex tissue variability.<n>Morph-ISR achieves the best LPIPS and ST-LPIPS among the evaluated methods.
  arXiv  Detail & Related papers  (2026-08-04T13:41:57Z)
• PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images [51.428093790826814]
  We propose pathology Autorgressive modeling (PathAR) to factorize structure and appearance for modality-label-conditioned pathology generation.<n>PathAR stabilizes morphology under heterogeneous modality-specific appearances and enables spatially aligned image-mask pair generation.
  arXiv  Detail & Related papers  (2026-06-01T01:43:48Z)
• Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers [57.54843029965778]
  Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail.<n>We propose In-Context Diffusion Transformer (IC-DiT), a layout-aware generative model that incorporates spatial layouts, textual descriptions, and visual embeddings into a unified diffusion transformer.<n>IC-DiT achieves higher fidelity, stronger spatial controllability, and better diagnostic consistency than existing methods.
  arXiv  Detail & Related papers  (2026-03-11T06:14:11Z)
• PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion [11.223559964746705]
  We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis.<n>PathoSyn reformulates imaging-pathology as a disentangled additive deviation on a stable anatomical manifold.<n>We show that PathoSyn significantly outperforms holistic diffusion and mask-conditioned baselines in both perceptual realism and anatomical fidelity.
  arXiv  Detail & Related papers  (2025-12-29T01:13:50Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model [23.9686884119236]
  We propose the Fine-grained Cardiac image Synthesis framework, established on 3D template conditional diffusion model.<n>FCaS achieves precise cardiac structure generation using template-guided Conditional Diffusion Model (TCDM)<n>To alleviate the confusion caused by imprecise synthetic images, we propose a Confidence-aware Adaptive Learning (CAL) strategy.
  arXiv  Detail & Related papers  (2025-03-12T17:25:09Z)
• URCDM: Ultra-Resolution Image Synthesis in Histopathology [4.393805955844748]
  Ultra-Resolution Cascaded Diffusion Models (URCDMs) are capable of synthesising entire histopathology images at high resolutions. We evaluate our method on three separate datasets, consisting of brain, breast and kidney tissue. URCDMs consistently generate outputs across various resolutions that trained evaluators cannot distinguish from real images.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.