Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological...

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Bibliographic Details
Published in:Experimental neurology 2021-05, Vol.339, p.113608-113608, Article 113608
Main Authors: Eitel, Fabian, Schulz, Marc-André, Seiler, Moritz, Walter, Henrik, Ritter, Kerstin
Format: Article
Language:English
Subjects:
Convolutional Neural Networks
Deep learning
MRI
Neuroimaging
Psychiatry
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Summary:By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
ISSN:0014-4886
1090-2430
DOI:10.1016/j.expneurol.2021.113608