Towards Computation- and Communication-efficient Computational Pathology
Journal:
arXiv
Published Date:
Apr 3, 2025
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.
