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Berkeley Lab 'minimalist machine learning' algorithms analyze images from very little data

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James Neely
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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.

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.

CAMERA is part of the lab's Computational Research Division.

"Our goal was to develop a technique that learns from a very small data set."

To make the algorithm accessible to a wide set of researchers, a Berkeley team led by Olivia Jain and Simon Mo built a web portal "Segmenting Labeled Image Data Engine (SlideCAM)" as part of the CAMERA suite of tools for DOE experimental facilities.

"In our laboratory, we are working to understand how cell structure and morphology influences or controls cell behavior.

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