Researchers at Google are using deep-learning convolutional neural networks to analyze retinal photos to predict a person's blood pressure, age, and smoking status, and a preliminary study suggests these networks can use this information to predict an individual's risk of an impending heart attack.
The research is one of several deep-learning applications that are boosting the simplicity and versatility of image processing.
Meanwhile, biologists at the Allen Institute for Cell Science are using convolutional neural networks to render flat, gray images of cells captured with light microscopes as three-dimensional images with colored organelles so cellular staining is unnecessary.
"What you're seeing now is an unprecedented shift in how well machine learning can accomplish biological tasks that have to do with imaging," says Anne Carpenter at the Broad Institute of the Massachusetts Institute of Technology and Harvard University.
Researchers also envision convolutional neural network-based image analysis as a method for inadvertently uncovering subtle biological phenomena.
From Scientific American
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