Digital pathology and detection of cancer based on hematoxylin and eosin (H&E) stained tissue samples has made enormous progress in the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via specific stains, novel microscopy techniques and spatial omics has made great leaps in terms of multiplexing capabilities and power to decipher spatial patterns of molecules and cells. They provide insight into cell development, micro-environment interactions, and transformation into diseased states. Yet, combining AI-based analysis of H&E data with specific stains and spatial omics is only at its very early stages. The purpose of this project is to bridge this gap through development of computational strategies combining digital pathology and function into Functional Pathology, with focus on cancer development.