Typical machine learning methods used to analyze experimental imaging data rely on tens or hundreds of thousands of training images. But Daniël Pelt and James Sethian of Berkeley Lab’s Center for Advanced Mathematics for Energy Research Applications (CAMERA) have developed what they call a “Mixed-Scale Dense Convolution Neural Network” (MS-D) that “learns” much more quickly from a remarkably small training set. One promising application of MS-D is in understanding the internal structure and morphology of biological cells to identify, for example, differences between healthy and diseased cells. In one such project in Carolyn Larabell’s lab, the method needed data from just seven cells to determine the cell structure.
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