Journal
EXPERIMENTAL NEUROLOGY
Volume 339, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.expneurol.2021.113608
Keywords
Deep learning; Convolutional Neural Networks; Psychiatry; Neuroimaging; MRI
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Funding
- German Research Foundation (DFG) [389563835, 402170461-TRR 265, 414984028-CRC 1404]
- Deutsche Multiple Sklerose Gesellschaft (DMSG)
- Manfred and Ursula-Muller Stiftung
- Brain & Behavior Research Foundation (NARSAD grant
- USA)
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This article introduces the application of deep neural networks in medical imaging for more accurate diagnostics and treatment recommendations, and discusses current challenges such as training models on small, biased data sets and algorithmic bias.
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.
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