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Introduction to Deep Learning in Clinical Neuroscience

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Machine Learning in Clinical Neuroscience

Part of the book series: Acta Neurochirurgica Supplement ((NEUROCHIRURGICA,volume 134))

Abstract

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.

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Acknowledgements

This project was partly funded by research grant from the Swedish Research Council (2017-00944).

Conflict of Interests

None declared.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden

    Eddie de Dios & Asgeir S. Jakola

  2. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Muhaddisa Barat Ali, Irene Yu-Hua Gu & Chenjie Ge

  3. Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden

    Tomás Gomez Vecchio & Asgeir S. Jakola

  4. Department of Neurosurgery, St. Olavs University Hospital HF, Trondheim, Norway

    Asgeir S. Jakola

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  1. Eddie de Dios
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  2. Muhaddisa Barat Ali
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  3. Irene Yu-Hua Gu
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  4. Tomás Gomez Vecchio
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  5. Chenjie Ge
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  6. Asgeir S. Jakola
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Correspondence to Asgeir S. Jakola .

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Editors and Affiliations

  1. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Victor E. Staartjes

  2. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Luca Regli

  3. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Carlo Serra

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de Dios, E., Ali, M.B., Gu, I.YH., Vecchio, T.G., Ge, C., Jakola, A.S. (2022). Introduction to Deep Learning in Clinical Neuroscience. In: Staartjes, V.E., Regli, L., Serra, C. (eds) Machine Learning in Clinical Neuroscience. Acta Neurochirurgica Supplement, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-85292-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-85292-4_11

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