Introduction to Deep Learning in Clinical Neuroscience
Book chapter, 2022

The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyzing MRI data and discuss potential applications and methodological caveats. Important aspects are data pre-processing, volumetric segmentation, and specific task-performing DL methods, such as CNNs and AEs. Additionally, GAN-expansion and domain mapping are useful DL techniques for generating artificial data and combining several smaller datasets. We present results of DL-based segmentation and accuracy in predicting glioma subtypes based on MRI features. Dice scores range from 0.77 to 0.89. In mixed glioma cohorts, IDH mutation can be predicted with a sensitivity of 0.98 and specificity of 0.97. Results in test cohorts have shown improvements of 5–7% in accuracy, following GAN-expansion of data and domain mapping of smaller datasets. The provided DL examples are promising, although not yet in clinical practice. DL has demonstrated usefulness in data augmentation and for overcoming data variability. DL methods should be further studied, developed, and validated for broader clinical use. Ultimately, DL models can serve as effective decision support systems, and are especially well-suited for time-consuming, detail-focused, and data-ample tasks.


Deep learning





Eddie de Dios

Sahlgrenska University Hospital

Muhaddisa Barat Ali

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Tomás Gomez Vecchio

University of Gothenburg

Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Asgeir Store Jakola

Sahlgrenska University Hospital

University of Gothenburg

Universitetssykehuset i Trondheim

Acta Neurochirurgica, Supplement


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Bioinformatics (Computational Biology)

Computer Science



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