SuperDiff：一种用于数字病理学的扩散超分辨率方法，具有综合质量评估

IF 11.8 1区医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Medical image analysis Pub Date : 2025-09-20 DOI:10.1016/j.media.2025.103808

Xuan Xu, Saarthak Kapse, Prateek Prasanna

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引用次数: 0

摘要

数字病理学在过去十年中取得了显著进展，全幻灯片图像（WSIs）包含了准确疾病诊断所必需的大量数据。高分辨率wsi对于精确诊断至关重要，但扫描设备的技术限制和载玻片制备的可变性会阻碍获得这些图像。超分辨率技术可以增强低分辨率图像；虽然生成对抗网络（GANs）在自然图像超分辨率任务中很有效，但由于过拟合和模式崩溃，它们经常受到组织病理学的困扰。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特异性评估方法。

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SuperDiff: A diffusion super-resolution method for digital pathology with comprehensive quality assessment

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 variability 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.

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来源期刊

Medical image analysis 工程技术-工程：生物医学

CiteScore

22.10

自引率

6.40%

发文量

309

审稿时长

6.6 months

期刊介绍： Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.