Deep Learning in Neuroimaging: Promises and challenges

Deep learning (DL) has been extremely successful when applied to the analysis of natural images. By contrast, analyzing neuroimaging data presents some unique challenges, including higher dimensionality, smaller sample sizes, multiple heterogeneous modalities, and a limited ground truth. In this article, we discuss DL methods in the context of four diverse and important categories in the neuroimaging field: classification/prediction, dynamic activity/connectivity, multimodal fusion, and interpretation/visualization. We highlight recent progress in each of these categories, discuss the benefits of combining data characteristics and model architectures, and derive guidelines for the use of DL in neuroimaging data. For each category, we also assess promising applications and major challenges to overcome. Finally, we discuss future directions of neuroimaging DL for clinical applications, a topic of great interest, touching on all four categories.

Automated summary

This article discusses the application of deep learning in four important categories in the field of neuroimaging, highlighting recent progress and challenges in each category. It also provides guidelines for using deep learning in neuroimaging data and explores the future directions of deep learning in clinical applications.