适应性压缩框架用于千兆像素整片幻灯片图像
Jonghyun Lee1,2, Lina Takemaru1,3,4, D M Bappy2
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nature communications
|December 3, 2025
概括
适应性压缩用于千兆像素全幻灯片图像 (AdaSlide) 减少了65-90%的存储需求,同时保持了诊断准确性. 这种由人工智能驱动的框架优化了数字病理图像的压缩,实现了高效的存储和人工智能开发.
科学领域:
- 数字病理学数字病理学
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 数字病理学中的千兆像素全幻灯片图像存在重大存储挑战.
- 传统的压缩方法是低效的,因为它们应用统一的比率,忽视图像区域的诊断重要性.
研究的目的:
- 为千兆像素全幻灯片图像 (AdaSlide) 引入适应性压缩,这是一个新的框架,用于高效和诊断性正确的图像压缩.
- 为了在整片图像中平衡压缩效率与诊断完整性.
主要方法:
- AdaSlide使用基于强化学习的压缩决策代理 (CDA) 来确定特定区域的压缩比.
- 集成了一个基础图像增强器 (FIE),以恢复压缩后的视觉真实性.
主要成果:
- 病理学家无法可靠地区分原始和AdaSlide恢复的图像 (55%的准确度),表明高感知准确度.
- 在各种任务中,存储需求减少到原始大小的10-35%.
- 在13个下游任务中保持了诊断性能.
结论:
- AdaSlide为存储数字病理图像提供了一个高效,可扩展和可靠的解决方案.
- 该框架将压缩策略与临床相关性结合起来,支持未来的病理学AI发展.
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