New study indicates that deep learning models are superior in biomedical research in comparison to standard machine learning.
"We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected," said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.
Findings were published in Nature Communications led by Georgia State University.
"If your application involves analyzing images or if it involves a large array of data that can't really be distilled into a simple measurement without losing information, deep learning can help," Plis said.. "These models are made for really complex problems that require bringing in a lot of experience and intuition."
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"Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better," he said.
"These models are learning on their own, so we can uncover the defining characteristics that they're looking into that allows them to be accurate," Abrol said. "We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look."
Source: Science Daily
