Deep learning in neuroimaging: Promises and challenges

Sergey Plis | Feb 24, 2022| Publications

Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun

Publication date : 2022/2/24

Journal : IEEE Signal Processing Magazine

Volume : 39

Issue : 2

Pages : 87-98

Publisher : IEEE

Description

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 …

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I received my Ph.D. degree in Computer Science in 2007 from The University of New Mexico, Albuquerque, NM, USA. Currently, I am an associate professor of CS at Georgia State University, and the Director of Machine Learning core at the TReNDS institute. My research interests within the fields of machine learning, AI, and data science lie in developing novel and applying existing techniques and approaches to analyzing large scale datasets. One of my key goals is to take advantage of the strengths of multiple data modalities and infer structure and patterns that are hard to obtain non-invasively and/or that are unavailable for direct observation. Ongoing work is focused on inferring multimodal probabilistic and causal descriptions based on fusion of fast and slow imaging modalities. This includes feature estimation via deep learning-based pattern recognition and learning causal graphical models. Besides academia, I’ve led AI research teams developing deep tech AI products including NLP and speech processing solutions that are currently shipped to customers. My research is supported by generous grants from NSF and NIH.

Deep learning in neuroimaging: Promises and challenges

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