Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network

TL;DR

Mew introduces a multiplex network framework with an efficient GNN and attention mechanism to improve analysis of multiplexed immunofluorescence images, addressing cellular heterogeneity and scalability issues.

Contribution

The paper presents Mew, a novel multiplex network-based framework with a scalable GNN and interpretable attention for improved mIF image analysis.

Findings

Mew outperforms existing methods on real-world datasets.

It effectively addresses cellular heterogeneity and scalability challenges.

The framework is computationally efficient and interpretable.

Abstract

Recent advancements in graph-based approaches for multiplexed immunofluorescence (mIF) images have significantly propelled the field forward, offering deeper insights into patient-level phenotyping. However, current graph-based methodologies encounter two primary challenges: (1) Cellular Heterogeneity, where existing approaches fail to adequately address the inductive biases inherent in graphs, particularly the homophily characteristic observed in cellular connectivity and; (2) Scalability, where handling cellular graphs from high-dimensional images faces difficulties in managing a high number of cells. To overcome these limitations, we introduce Mew, a novel framework designed to efficiently process mIF images through the lens of multiplex network. Mew innovatively constructs a multiplex network comprising two distinct layers: a Voronoi network for geometric information and a Cell-type network for capturing cell-wise homogeneity. This framework equips a scalable and efficient Graph Neural Network (GNN), capable of processing the entire graph during training. Furthermore, Mew integrates an interpretable attention module that autonomously identifies relevant layers for image classification. Extensive experiments on a real-world patient dataset from various institutions highlight Mew's remarkable efficacy and efficiency, marking a significant advancement in mIF image analysis. The source code of Mew can be found here: \url{https://github.com/UNITES-Lab/Mew}