Mixed Magnification Aggregation for Generalizable Region-Level Representations in Computational Pathology

TL;DR

This paper introduces a mixed magnification region-level aggregation method in computational pathology that fuses multi-resolution features to improve biomarker prediction across various cancer types.

Contribution

It proposes a novel region-level mixing encoder that combines multi-magnification tile representations and pretrains them with masked embedding modeling for better generalization.

Findings

Improved predictive performance on biomarker tasks across cancer types.

Cancer-dependent variations in model improvements.

Highlights the importance of spatial context in pathology analysis.

Abstract

In recent years, a standard computational pathology workflow has emerged where whole slide images are cropped into tiles, these tiles are processed using a foundation model, and task-specific models are built using the resulting representations. At least 15 different foundation models have been proposed, and the vast majority are trained exclusively with tiles using the 20$\times$ magnification. However, it is well known that certain histologic features can only be discerned with larger context windows and requires a pathologist to zoom in and out when analyzing a whole slide image. Furthermore, creating 224$\times$224 pixel crops at 20$\times$ leads to a large number of tiles per slide, which can be gigapixel in size. To more accurately capture multi-resolution features and investigate the possibility of reducing the number of representations per slide, we propose a region-level mixing encoder. Our approach jointly fuses image tile representations of a mixed magnification foundation model using a masked embedding modeling pretraining step. We explore a design space for pretraining the proposed mixed-magnification region aggregators and evaluate our models on transfer to biomarker prediction tasks representing various cancer types. Results demonstrate cancer dependent improvements in predictive performance, highlighting the importance of spatial context and understanding.