ConGAT: Context-aware graph attention network for 3D region of interest discovery in multiplexed microscopy images

We present ConGAT, CON text-aware G raph AT tention Network, a visual analysis system for identifying and analyzing cell–cell interactions in large tissue volumes, which leverages graph attention networks and self-supervised learning. Advanced multichannel microscopy techniques, such as Cyclic Immunofluorescence (CycIF), enable detailed, biomarker-based single-cell mapping, but yield large, densely packed volumes that are difficult to explore. ConGAT implements a machine learning-assisted visual analysis approach for these data. First, ConGAT constructs 3D spatial graphs from volumetric image data and applies a 3D graph attention network to model cell–cell relationships directly in 3D space. Next, we introduce a self-supervised feature refinement strategy that learns discriminative region-of-interest representations without labeled data. Through an interactive visual interface, ConGAT allows experts to define arbitrary combinations of biomarkers, enabling flexible ROIs identification and iterative exploration of spatial patterns. We assess ConGAT on synthetic ground truth and on real-world data. An evaluation with domain expert validation demonstrates that ConGAT identifies spatial patterns and candidate ROIs that show substantial agreement with expert assessments of biological relevance.