Towards Computation- and Communication-efficient Computational Pathology

• URL: http://arxiv.org/abs/2504.02628v2
• Date: Tue, 03 Jun 2025 13:05:58 GMT
• Title: Towards Computation- and Communication-efficient Computational Pathology
• Authors: Chu Han, Bingchao Zhao, Jiatai Lin, Shanshan Lyu, Longfei Wang, Tianpeng Deng, Cheng Lu, Changhong Liang, Hannah Y. Wen, Xiaojing Guo, Zhenwei Shi, Zaiyi Liu,
• Abstract summary: We present a novel- and communication-efficient framework called Magni-Aligned Global-Local Transformer (MAG-GLTrans)<n>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.
• Score: 15.697238830981927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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