标题： 迈向高效计算的计算病理学 显示英文标题

标题： Towards Computation- and Communication-efficient Computational Pathology

摘要： 尽管当前计算病理学模型在广泛的应用场景中表现出色，但由于它们依赖于高放大倍率的全片图像分析，因此在诊断效率方面面临重大挑战。 这一局限性严重限制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，我们提出了一种新颖的计算和通信高效框架，称为放大倍率对齐全局-局部Transformer（MAG-GLTrans）。 我们的方法通过使用低放大倍率输入而不是高放大倍率输入来进行有效的分析，从而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倍率对齐（MAG）机制，该机制利用自监督学习通过有效地对齐低和高放大倍率级别的特征表示来弥合信息差距。 通过对各种基本CPath任务的广泛评估，MAG-GLTrans展示了最先进的分类性能，并取得了显著的效率提升：计算时间减少高达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展示了我们的MAG框架的多功能性：（1）作为特征提取器以提高任何CPath架构的效率的适用性，以及（2）与现有的基础模型和组织病理学特定编码器的兼容性，使它们能够以最小的信息损失处理低放大倍率输入。 这些进步使MAG-GLTrans成为时间敏感应用的特别有前景的解决方案，特别是在术中冷冻切片诊断的背景下，准确性和效率至关重要。 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 (MAG-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 (MAG) 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, MAG-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 MAG 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 MAG-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.