Deep Learning Outperforms Standard Machine Learning at Brain Imaging Analysis, Finds New Study

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study.

At the Center for Translational Research in Neuroimaging and Data Science (TReNDS; Atlanta, GA, USA), researchers are using deep learning to learn more about how mental illness and other disorders affect the brain. Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood. Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources.


Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data. However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantage—the ability to learn from the data with little to no preprocessing. In a comparative study of representative models from classical machine learning and deep learning, the researchers found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

In some cases, the researchers found that standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patient’s body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches. The downside of deep learning models is they are “data hungry” at the outset and must be trained on lots of information. But once these models are trained, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers. The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, it’s clear these models outperform standard machine learning models in many settings.

“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. “Deep learning’s promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques.”

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Center for Translational Research in Neuroimaging and Data Science