标题： 面向计算和通信高效的计算病理学 显示英文标题

标题： Towards Computation- and Communication-efficient Computational Pathology

摘要： 尽管在各种应用中表现出色，当前的计算病理学模型由于依赖高倍率全幻灯片图像分析，面临着显著的诊断效率挑战。 这一限制严重削弱了它们的临床实用性，尤其是在时间敏感的诊断场景和需要高效数据传输的情况下。 为了解决这些问题，我们提出了一种新的计算和通信高效的框架，称为放大率对齐全局局部变换器（MAGA-GLTrans）。 我们的方法通过使用低倍率输入而不是高倍率输入进行有效分析，显著减少了计算时间、文件传输需求和存储开销。 关键创新在于我们提出的放大率对齐（MAGA）机制，该机制通过自我监督学习有效地对齐其特征表示，以弥合低倍率和高倍率水平之间的信息差距。 通过在各种基础CPath任务上的广泛评估，MAGA-GLTrans展示了最先进的分类性能，同时实现了显著的效率提升：计算时间最多减少10.7倍，文件传输和存储需求减少超过20倍。 此外，我们通过两个重要的扩展突出了我们MAGA框架的多功能性：（1）它作为特征提取器的应用，可以提高任何CPath架构的效率，以及（2）它与现有基础模型和组织病理学专用编码器的兼容性，使它们能够以最小的信息损失处理低倍率输入。 这些进展使MAGA-GLTrans成为时间敏感应用的一个特别有前景的解决方案，特别是在术中冰冻切片诊断的背景下，准确性和效率都是至关重要的。 显示英文摘要

摘要： Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis. This limitation severely compromises their clinical utility, especially in time-sensitive diagnostic scenarios and situations requiring efficient data transfer. To address these issues, we present a novel computation- and communication-efficient framework called Magnification-Aligned Global-Local Transformer (MAGA-GLTrans). Our approach significantly reduces computational time, file transfer requirements, and storage overhead by enabling effective analysis using low-magnification inputs rather than high-magnification ones. The key innovation lies in our proposed magnification alignment (MAGA) mechanism, which employs self-supervised learning to bridge the information gap between low and high magnification levels by effectively aligning their feature representations. Through extensive evaluation across various fundamental CPath tasks, MAGA-GLTrans demonstrates state-of-the-art classification performance while achieving remarkable efficiency gains: up to 10.7 times reduction in computational time and over 20 times reduction in file transfer and storage requirements. Furthermore, we highlight the versatility of our MAGA framework through two significant extensions: (1) its applicability as a feature extractor to enhance the efficiency of any CPath architecture, and (2) its compatibility with existing foundation models and histopathology-specific encoders, enabling them to process low-magnification inputs with minimal information loss. These advancements position MAGA-GLTrans as a particularly promising solution for time-sensitive applications, especially in the context of intraoperative frozen section diagnosis where both accuracy and efficiency are paramount.