Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network

Sukwon Yun, Jie Peng, Alexandro E Trevino, Chanyoung Park, Tianlong Chen* ;

"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: 172 Cellular Heterogeneity, where existing approaches fail to adequately address the inductive biases inherent in graphs, particularly the homophily characteristic observed in cellular connectivity; and 173 Scalability, where handling cellular graphs from high-dimensional images faces difficulties in managing a high number of cells. To overcome these limitations, we introduce , a novel framework designed to efficiently process mIF images through the lens of multiplex network. 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, 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 ’s remarkable efficacy and efficiency, marking a significant advancement in mIF image analysis. The source code of can be found here: https://github. com/UNITES-Lab/Mew"