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Attention-based ROI Discovery in 3D Tissue Images
Authors: Fathollahian, H., Zhao, S., Nipu, N., Marai, G.E.
Publication: IEEE Vis 2025 (Bio+MedVis Challenge Poster), Vienna, Austria, IEEE URL: http://biovis.net/2025/biovisChallenges_vis/ High-dimensional tissue imaging generates highly complex 3D data containing multiple biomarkers, making it challenging to identify biologically relevant regions without an expert user specifying manual labels for regions of interest. We introduce an approach to automatically identifying regions of interest (ROIs) in the 3D microscopy data. Our approach is based on a novel self-supervised multi-layer graph attention network (SSGAT), coupled with a React interactive interface wrapped around Vitessce. SSGAT uses an adversarial self-supervised learning objective to discover meaningful immune microenvironments across marker interactions. Our method reveals complex spatial bioreactions that can be visually assessed to assess their distribution across tissue. Index Terms: Biomedical visualization, graph attention networks, self-supervised learning, spatial interaction analysis. Date: November 2, 1970 - November 7, 1970 Document: View PDF |
