Towards Computation- and Communication-efficient Computational Pathology

2025-04-03

AI Summary

Current computational pathology (CPath) models rely on high-magnification whole-slide images (WSIs), incurring prohibitive computational overhead and substantial storage/transmission costs—severely hindering clinical deployment, especially in time-critical intraoperative frozen-section scenarios. To address this, we propose the Magnification-Aligned Global-Local Transformer (MAGA-GLTrans), the first framework enabling high-accuracy diagnosis using only low-magnification WSIs. Its core innovation is a self-supervised Magnification Alignment (MAGA) mechanism that effectively bridges the feature gap between low- and high-magnification representations. Coupled with a global-local Transformer architecture, MAGA-GLTrans supports plug-and-play adaptation to arbitrary CPath or foundation models. On multiple benchmark tasks, it achieves state-of-the-art performance while reducing inference latency by 10.7× and compressing file size by over 20×—significantly enhancing clinical practicality and deployment efficiency.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Learning on the Edge & Model Compression
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

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 (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.

Problem

• Reduces computational time for pathology models
• Minimizes file transfer and storage needs
• Enables efficient low-magnification image analysis

Innovation

• Uses low-magnification inputs for efficiency
• MAGA mechanism aligns feature representations
• Reduces computation time and storage