Overview of learning-based models to enhance the imaging infrastructure

The development of microscopic technologies has enhanced the translational research between scientists, engineers, biologists, and biomedical researchers, enabling the visualization and evaluation of complex biological systems. Advances in imaging systems are essential to further develop our understanding of cellular mechanisms and apply them to new diagnostic methods and disease treatment. Over the last decade, machine learning has been heavily used in microscopy image analysis, from the classification of cells to the reconstruction of real-time images, empowering the toolbox of automated microscopy. In this invited contribution, we discuss the use of generative adversarial networks in quantitative phase imaging and super-resolution microscopy.