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

delete2025-09-20
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PRE
AI
X
Xuan Xu *
S
Saarthak Kapse
P
Prateek Prasanna
DOI：10.1016/j.media.2025.103808delete
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Abstract

Abstract

En 中文
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.

Journal

Medical Image Analysis cover
Medical Image Analysis
IF:
11.8
Papers:
3.8K
Citations:
2.4W

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No organization information available