Recent advances in imaging and machine learning have increased our ability to capture information about biological systems in the form of images. Given that spatial genomics allows us to capture both the “parts list” and spatial variation in living systems, images have the potential to be a universal image type for biology. In this talk, we describe the field’s progress towards such a vision. We describe our experience using live-cell imaging and end-point spatial genomics to integrate heterogeneous measurements in single cells and highlight the common computer vision challenges raised by this approach. We discuss how these problems might be solved using modern deep learning methods. We conclude by describing our latest work on using weakly supervised learning to perform spot detection in multiplexed RNA FISH experiments.