Eric Wu

Eva is a foundation model for tissue imaging data that learns multi-scale spatial representations and performs various tasks like cross-modal inference and patient stratification.

ROSIE generates multiplex immunofluorescence staining from H&E images by imputing protein expression and localization using deep learning.

The paper introduces a metric to measure surface protein polarity from immunofluorescence imaging and shows it identifies immune cell states linked to tumor engagement, improving survival prediction models.

This work introduces SCGP, a method for unsupervised identification of tissue structures from spatial omics data, and demonstrates its application to spatial transcriptomics and proteomics datasets.

7-UP generates in silico 40-plex CODEX data from a 7-plex mIF panel using cellular morphology, enabling cell type classification and survival prediction across diverse samples.

SPACE-GM identifies spatial motifs in cancer samples linked to recurrence and immunotherapy outcomes using geometric deep learning.