  This paper introduces a new method of data-driven microscope design for
virtual fluorescence microscopy. Our results show that by including a model of
illumination within the first layers of a deep convolutional neural network, it
is possible to learn task-specific LED patterns that substantially improve the
ability to infer fluorescence image information from unstained transmission
microscopy images. We validated our method on two different experimental
setups, with different magnifications and different sample types, to show a
consistent improvement in performance as compared to conventional illumination
methods. Additionally, to understand the importance of learned illumination on
inference task, we varied the dynamic range of the fluorescent image targets
(from one to seven bits), and showed that the margin of improvement for learned
patterns increased with the information content of the target. This work
demonstrates the power of programmable optical elements at enabling better
machine learning algorithm performance and at providing physical insight into
next generation of machine-controlled imaging systems.
