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

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xuan Xu, Saarthak Kapse, Prateek Prasanna
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引用次数: 0

Abstract

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.
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
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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来源期刊
Medical image analysis
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.
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