Histo-Diffusion：一种用于数字病理学且具备全面质量评估的扩散超分辨率方法
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment
AI总结：
本文提出Histo-Diffusion，一种用于数字病理学图像超分辨率的扩散方法，包含组织病理学先验恢复模块和可控扩散模块，并设计了结合全参考和无参考指标的综合评估策略，在多个数据集上优于GANs，支持多分辨率生成以辅助诊断。
摘要：
在过去十年中，数字病理学取得了显著进展，其中全切片图像（WSIs）包含了大量对准确疾病诊断至关重要的数据。高分辨率WSIs对于精确诊断必不可少，但扫描设备的技术限制和切片制备的变异性可能会阻碍获取这些图像。超分辨率技术可以增强低分辨率图像；尽管生成对抗网络（GANs）在自然图像超分辨率任务中表现有效，但由于过拟合和模式崩溃，它们在组织病理学中常常遇到困难。传统的评估指标在评估组织病理学图像的复杂特征方面存在不足，因此需要稳健的组织病理学专用评估方法。我们引入了Histo-Diffusion，这是一种专门为数字病理学中生成和评估超分辨率图像而设计的新型基于扩散的方法。它包括一个用于组织病理学先验的恢复模块和一个用于生成高质量图像的可控扩散模块。我们精心整理了两个组织病理学数据集，并提出了一种综合评估策略，该策略结合了全参考和无参考指标，以彻底评估数字病理学图像的质量。在多个数据集上与最先进方法进行的比较分析表明，Histo-Diffusion优于GANs。我们的方法为组织病理学图像超分辨率提供了一种通用的解决方案，能够处理来自不同输入尺寸的多分辨率生成，为诊断过程提供了有价值的支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
