Pathology Image Restoration via Mixture of Prompts

2025-03-16

AI Summary

解决数字病理学中单焦平面扫描图像质量低的问题，提出基于变压器和扩散模型的两阶段修复方法，通过混合提示提高图像恢复质量。

Technology Category

• Computer Vision: Medical and Biological Imaging
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In digital pathology, acquiring all-in-focus images is essential to high-quality imaging and high-efficient clinical workflow. Traditional scanners achieve this by scanning at multiple focal planes of varying depths and then merging them, which is relatively slow and often struggles with complex tissue defocus. Recent prevailing image restoration technique provides a means to restore high-quality pathology images from scans of single focal planes. However, existing image restoration methods are inadequate, due to intricate defocus patterns in pathology images and their domain-specific semantic complexities. In this work, we devise a two-stage restoration solution cascading a transformer and a diffusion model, to benefit from their powers in preserving image fidelity and perceptual quality, respectively. We particularly propose a novel mixture of prompts for the two-stage solution. Given initial prompt that models defocus in microscopic imaging, we design two prompts that describe the high-level image semantics from pathology foundation model and the fine-grained tissue structures via edge extraction. We demonstrate that, by feeding the prompt mixture to our method, we can restore high-quality pathology images from single-focal-plane scans, implying high potentials of the mixture of prompts to clinical usage. Code will be publicly available at https://github.com/caijd2000/MoP.

Problem

• Restores high-quality pathology images from single-focal-plane scans.
• Addresses intricate defocus patterns and domain-specific semantic complexities.
• Proposes a novel mixture of prompts for improved image restoration.

Innovation

• Two-stage restoration with transformer and diffusion model
• Mixture of prompts for defocus and semantic modeling
• High-quality pathology image restoration from single-focal-plane scans