I will present our new computer vision algorithm, ST-Net, which can computationally synthesize spatially resolved transcriptomics directly from H&E histology images (He et al. Nature Biomedical Engineering 2020). I will demonstrate ST-Net on breast cancer data, and show how it characterizes tumor spatial heterogeneity and quantifies tumor-immune interactions. I will then discuss how similar algorithms can capture complex morphological changes over time of individual cells.
1. Understand how deep learning can integrate imaging with genomics
2. Understand how to characterize spatial and temporal variation in cells and connect it with human diseases