Digital pathology has advanced significantly over the last decade, with Whole Slide Images (WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High-resolution WSIs are essential for precise diagnosis but technical limitations in scanning equipment and variablity in slide preparation can hinder obtaining these images. Super-resolution techniques can enhance low-resolution images; while Generative Adversarial Networks (GANs) have been effective in natural image super-resolution tasks, they often struggle with histopathology due to overfitting and mode collapse. Traditional evaluation metrics fall short in assessing the complex characteristics of histopathology images, necessitating robust histology-specific evaluation methods. We introduce Histo-Diffusion, a novel diffusion-based method specially designed for generating and evaluating super-resolution images in digital pathology. It includes a restoration module for histopathology prior and a controllable diffusion module for generating high-quality images. We have curated two histopathology datasets and proposed a comprehensive evaluation strategy which incorporates both full-reference and no-reference metrics to thoroughly assess the quality of digital pathology images. Comparative analyses on multiple datasets with state-of-the-art methods reveal that Histo-Diffusion outperforms GANs. Our method offers a versatile solution for histopathology image super-resolution, capable of handling multi-resolution generation from varied input sizes, providing valuable support in diagnostic processes.

本研究解决了高分辨率全幻灯片图像（WSIs）在数字病理学中获取困难的问题，提出了Histo-Diffusion方法。这是一种新颖的扩散基础方法，能够有效提升低分辨率病理图像质量，并建立了一套全面的评估策略。研究结果显示，Histo-Diffusion在多个数据集上的表现优于现有的生成对抗网络（GANs），为病理图像的超分辨率生成提供了有效支持。

Histo-Diffusion：一种用于数字病理学的扩散超分辨率方法及综合质量评估