Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images

TL;DR

This paper introduces a novel permutation-invariant method for whole slide image classification that reduces computational complexity by distilling gigapixel images into single, informative vectors, improving efficiency and accuracy in digital pathology.

Contribution

The paper presents a new approach using clustered patch embeddings and permutation-invariant models to efficiently analyze large WSIs, a significant advancement over existing methods.

Findings

Enhanced computational efficiency in WSI analysis

Accurate classification with reduced data complexity

Effective representation of gigapixel images

Abstract

Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field.