Benjamin Judkewitz

Roarke Horstmeyer, Richard Y. Chen, Barbara Kappes et al.

Deep learning algorithms offer a powerful means to automatically analyze the content of medical images. However, many biological samples of interest are primarily transparent to visible light and contain features that are difficult to resolve with a standard optical microscope. Here, we use a convolutional neural network (CNN) not only to classify images, but also to optimize the physical layout of the imaging device itself. We increase the classification accuracy of a microscope's recorded images by merging an optical model of image formation into the pipeline of a CNN. The resulting network simultaneously determines an ideal illumination arrangement to highlight important sample features during image acquisition, along with a set of convolutional weights to classify the detected images post-capture. We demonstrate our joint optimization technique with an experimental microscope configuration that automatically identifies malaria-infected cells with 5-10% higher accuracy than standard and alternative microscope lighting designs.

Roarke Horstmeyer, Benjamin Judkewitz, Ivo Vellekoop et al.

We describe an encrypted communication principle that can form a perfectly secure link between two parties without electronically saving either of their keys. Instead, cryptographic key bits are kept safe within the unique mesoscopic randomness of two volumetric scattering materials. We demonstrate how a shared set of patterned optical probes can generate 10 gigabits of statistically verified randomness between a pair of unique 2 cubic millimeter scattering objects. This shared randomness is used to facilitate information-theoretically secure communication following a modified one-time pad protocol. Benefits of volumetric physical storage over electronic memory include the inability to probe, duplicate or selectively reset any random bits without fundamentally altering the entire key space. Beyond the demonstrated communication scheme, our ability to securely couple the randomness contained within two unique physical objects may help strengthen the hardware for a large class of cryptographic protocols, which is currently a critically weak link in the security pipeline of our increasingly mobile communication culture.