'Minimalist machine learning' algorithm analyzes complex microscopy and other images from very little data

Mathematicians at Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a radical new approach to machine learning: a new type of highly efficient "deep convolutional neural network" that can automatically analyze complex experimental scientific images from limited data.* As experimental facilities generate higher-resolution images at higher speeds, scientists struggle to manage and analyze the resulting data, which is often done painstakingly by hand. For example, biologists record cell images and painstakingly outline the borders and structure by hand. One person may spend weeks coming up with a single fully three-dimensional image of a cellular structure. Or materials scientists use tomographic reconstruction to peer inside rocks and materials, and then manually label different regions, identifying cracks, fractures, and voids by hand.