Recent advancements in graph-based approaches for multiplexed immunofluorescence (mIF) images have significantly propelled the field forward, offering deeper insights into patient-level phenotype prediction. 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 m^2IF, a novel multiplex network framework designed to efficiently process mIF images. m^2IF 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, m^2IF 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 m^2IF’s remarkable efficacy and efficiency, marking a significant advancement in mIF analysis. m^2IF not only addresses the prevalent challenges in graph-based ML for mIF images but also establishes a new benchmark for accuracy and scalability in the domain.