Matthew F Bieniosek

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.

HOPE learns tumor microenvironment signatures from paired H&E and spatial omics data to improve prognostic stratification using H&E images alone.

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

This study uses spatial multi-omics and deep learning to identify immunotherapy response and resistance signatures in hepatocellular carcinoma.

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

The paper introduces emObject, a domain-specific data abstraction for spatial biology, demonstrating its utility for various spatial omics data types.