'Minimalist machine learning' algorithms analyze images from very little data: CAMERA researchers develop highly efficient neural networks for analyzing experimental scientific images from limited training data

Daniël Pelt and James Sethian of Berkeley Lab's Center for Advanced Mathematics for Energy Research Applications (CAMERA) turned the usual machine learning perspective on its head by developing what they call a "Mixed-Scale Dense Convolution Neural Network (MS-D)" that requires far fewer parameters than traditional methods, converges quickly, and has the ability to "learn" from a remarkably small training set. Their approach is already being used to extract biological structure from cell images, and is poised to provide a major new computational tool to analyze data across a wide range of research areas. As experimental facilities generate higher resolution images at higher speeds, scientists can struggle to manage and analyze the resulting data, which is often done painstakingly by hand. In 2014, Sethian established CAMERA at Berkeley Lab as an integrated, cross-disciplinary center to develop and deliver fundamental new mathematics required to capitalize on experimental investigations at DOE Office of Science user facilities. CAMERA is part of the lab's Computational Research Division.