This page is dedicated to deep learning algorithms that design new types of hardware. The primary focus of the work presented here is to showcase new and improved imaging systems (cameras, microscopes, CT, MRI), which are specifically optimized to collect data by and for deep learning tasks. Please find an introduction to this area of research here, and several example projects demonstrating this new effort below.
This website includes research from the Computational Optics Lab at Duke University. We develop new microscopes, cameras and computer algorithms to capture better biomedical images. The lab is directed by Dr. Roarke Horstmeyer , who is an Assistant Professor in the Department of Biomedical Engineering at Duke
The Duke University engineering course, Machine Learning and Imaging, is now fully online. This class is an overview of machine learning and imaging science, with a focus on the intersection of the two fields. It offers much of the introductory material needed to understand the basics of machine learning for hardware design. Please find lectures, homeworks, example code and the course content here
We present a novel microscopy approach: Multi Camera Array Microscopy (MCAM), for rapid acquisition of Gigapixel Videos and Images. Also capture 3D Depth measurement, and analyse large FOV sample such as 48 well plates and live freely moving model organisms, with inbuilt algorithms
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.
Parallelized Diffuse Correlation Spectrocopy
With a high-sensitivity DCS system with 1024 parallel detection channels integrated within a SPAD array, we demonstrate the ability to detect mm-scale perturbations up to 1 cm deep within a tissue-like phantom . We also measure the human pulse at high fidelity and detect behaviorally-induced physiological variations from the human prefrontal cortex.
APL Photonics (2020)
To increase the throughput of current pathology labs we present an imaging system that simultaneously captures multiple images across a large effective field-of-view, overlaps these images onto a common detector, and then automatically classifies the overlapped image’s contents to increase malaria parasite detection throughput.
We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination.
To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network
Biomedical Optics Express(2019)
In this work, we investigate an approach to jointly optimize multiple microscope settings, together with a classification network, for improved performance with such automated tasks.
Illumination within a microscope can drastically alter information captured by the image sensor. We present a reinforcement learning system that adaptively explores optimal patterns to illuminate specimens for immediate classification.