For centuries, astronomical imaging has focused on detecting and studying the faintest, (angularly) smallest features possible in the sky. As a consequence, astronomers have developed imaging and spectroscopic systems aligned with the associated challenges of faint signals, high/variable backgrounds, complex scenes, source contamination, distorted wavefronts, among others. This in turn has led to the development of advanced techniques in hardware, calibration, and data analysis. In recent years, biological and medical microscopy have evolved to experience many similar challenges so that a number of established astronomical techniques show promise for deployment in this field. I will give an overview of some of our group’s work in applying astronomical techniques to biomedical imaging, including: advanced image processing; machine learning and artificial intelligence algorithms for source detection, tracking and classification; hyperspectral imaging via integral field spectroscopy. I will also present our new work on a low-cost technique to provide sub-diffraction resolution with hyperspectral imaging on standard biological microscopes.