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释放数字病理学的潜力:压缩的新基线

Maximilian Fischer^1,2,3,4, Peter Neher^1,2,5,6, Peter Schüffler^7,8

¹Institute Division of Medical Image Computing, German Cancer Research Center, (DKFZ), Heidelberg, Germany.

Journal of pathology informatics

|March 10, 2025

概括

数字病理学面临的挑战是大整体幻灯片图像 (WSI) 文件大小. 这项研究引入了一种新的指标来评估损耗压缩,改善了数字病理学的采用.

科学领域:

数字病理学数字病理学
医学成像医学成像
图像压缩图像压缩

背景情况:

在数字病理学中,整个幻灯片图像 (WSIs) 很大,需要压缩.
像JPEG这样的当前损耗压缩方法可以改变图像质量,影响临床决策.
之前的研究分别评估了感知质量和下游任务性能.

研究的目的:

在WSIs上共同评估损耗压缩方案的感知质量和下游任务性能.
开发一种用于评估WSI压缩的新型标准化度量.
通过改进压缩评估来加速数字病理学的临床采用.

主要方法:

在四个数据集上对压缩方案的联合评估.
收集未压缩的数据集,以进行无偏见的感知评估.
开发一种新的特征相似度指标,用于评估压缩方案.
与传统方法 (JPEG-XL,WebP) 相比,微调感知质量的深度学习模型的比较.

主要成果:

对感知质量进行微调的深度学习模型显示出有希望的结果,但受到了训练数据偏差和不良概括的影响.
像JPEG-XL和WebP这样的传统压缩方案被微调的模型所超越,以进一步实现WSI压缩.
新型特征相似度指标与压缩WSIs的实际下游性能有很强的对齐.
拟议的指标提供了一个标准化的方法,减少了对特定任务的评估的需求.

结论:

对损耗压缩方案的统一评估对于数字病理学至关重要.
新的特征相似度指标为评估WSI压缩提供了通用和标准化的方法.
这一指标可以减轻独立下游任务评估的需要,简化评估.
改进的压缩评估方法将促进数字病理学的临床整合.

关键词:

特性相似性特性相似性有损压缩的压缩.整个幻灯片图像的图像.

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