1145-Kasumi: Learning Persistent Patterns in Spatial Data

Paper Talk · Jun 17, 2026 · 22m

This paper introduce Kasumi, a novel computational framework designed to analyze spatial omics data by identifying persistent local patterns within tissues. Unlike traditional methods that rely solely on cell-type clustering, Kasumi uses unsupervised multi-view modeling to capture complex, non-linear relationships between cells and their molecular markers. This approach allows researchers to represent tissues as a collection of meaningful neighborhoods that remain consistent across different samples and clinical conditions. The research demonstrates that Kasumi significantly improves patient…