Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images

arXiv:2609.24116 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/PathfinderLab/RestorePath

Terminology

• Diffusion Model — 10× in this paper · explained in 8 episodes
• Latent Diffusion Model — 8× in this paper · explained in 2 episodes
• AUC — 7× in this paper · explained in 2 episodes
• Computational Pathology — 7× in this paper
• Embeddings — 7× in this paper · explained in 2 episodes
• Large Kernel Attention — 7× in this paper
• Adaptive Guidance Scale — 5× in this paper
• Whole Slide Image — 5× in this paper
• Multiple Instance Learning — 4× in this paper
• Quality Control — 4× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model (LDM) — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adjusted Rand Index — 1× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Enhanced by Distance-Weighted Interpolation — 1× in this paper
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Multiple Instance Learning (MIL) — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Pathology Foundation Model — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models