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Introduction to Dee...
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de Dios, EddieSahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
(författare)
Introduction to Deep Learning in Clinical Neuroscience
- Artikel/kapitelEngelska2022
Förlag, utgivningsår, omfång ...
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2021-12-04
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Cham :Springer International Publishing,2022
Nummerbeteckningar
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LIBRIS-ID:oai:research.chalmers.se:7cc0cfbd-95dd-49dc-95ee-5ffd6a6d25c1
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https://doi.org/10.1007/978-3-030-85292-4_11DOI
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https://research.chalmers.se/publication/527721URI
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https://gup.ub.gu.se/publication/310675URI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:kap swepub-publicationtype
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Ämneskategori:vet swepub-contenttype
Anmärkningar
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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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Ali, Muhaddisa Barat,1986Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)barat
(författare)
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Gu, Irene Yu-Hua,1953Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)irenegu
(författare)
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Vecchio, Tomás GomezGothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience(Swepub:gu)xgmeto
(författare)
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Ge, Chenjie,1991Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)chenjie
(författare)
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Jakola, Asgeir StoreGothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience(Swepub:gu)xjakas
(författare)
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Sahlgrenska universitetssjukhusetChalmers tekniska högskola
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Acta Neurochirurgica, SupplementCham : Springer International Publishing134, s. 79-892197-83950065-1419
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