Deep learning algorithms offer a powerful means to automatically analyze the content of biomedical images. However, many biological samples of interest are difficult to resolve with a standard optical microscope. Either they are too large to fit within the microscope's field-of-view, or too thick, or are quickly moving around. In this talk, I will discuss our recent work in addressing these challenges by using deep learning algorithms to design new experimental strategies for microscopic imaging. Specifically, we use deep neural networks to jointly optimize the physical parameters of our computational microscopes - their illumination settings, lens layouts and data transfer pipelines, for example - for specific tasks. Examples include learning specific illumination patterns that can improve classification of the malaria parasite by up to 15%, and establishing fast methods to automatically track moving specimens across gigapixel-sized images.
Roarke Horstmeyer is a new assistant professor within the Biomedical Engineering Department at Duke University. He develops microscopes, cameras and computer algorithms for a wide range of applications, from forming 3D reconstructions of organisms to detecting neurons deep within tissue. Most recently, Dr. Horstmeyer was a guest professor at the University of Erlangen in Germany and an Einstein International Postdoctoral Fellow at Charitè Medical School in Berlin. Prior to his time in Germany, Dr. Horstmeyer earned a PhD from Caltech's EE department (2016), an MS from the MIT Media Lab (2011), and bachelor's degrees in physics and Japanese from Duke in 2006.