A Magnification Alignment Framework Enables Computation- and Communication-Efficient Computational Pathology

Despite the impressive performance across a range of applications, current computational pathology (CPath) models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis. This limitation compromises their clinical utility, especially in time-sensitive diagnostic scenarios and situations requiring efficient data transfer. To address these issues, we developed a computation- and communication-efficient framework called Magnification-Aligned Global-Local Transformer (MAG-GLTrans). The approach substantially reduced computational time, file transfer requirements, and storage overhead by enabling effective analysis using low-magnification inputs. The magnification alignment (MAG) mechanism employed 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 demonstrated state-of-the-art classification performance while achieving remarkable efficiency gains, including up to a 10.7× reduction in computational time and more than a 20× reduction in file transfer and storage requirements. Furthermore, the versatility of the MAG framework was demonstrated through two significant extensions: (i) its applicability as a feature extractor to enhance the efficiency of any CPath architecture and (ii) its compatibility with existing foundation models, enabling them to process low-magnification inputs with minimal information loss. In a real-world clinical application, computer-assisted telepathology for intraoperative frozen section diagnosis of non-small cell lung carcinoma, MAG-GLTrans effectively recognized distinct tumor morphologies and accurately localized diagnostically relevant regions with low computational and communication costs. Together, these advancements position MAG-GLTrans as a particularly promising solution for time-sensitive applications, especially in the context of intraoperative frozen section diagnosis, in which both accuracy and efficiency are paramount.

MAG-GLTrans is a general-purpose whole-slide image analysis architecture designed for computational pathology applications, including cancer subtyping, metastasis detection, and mutation prediction, that simultaneously optimizes diagnostic capability, computational efficiency, and communication burden.