Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy-Reference-Cited by-同舟云学术

Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy

Published:2019-11-06 Issue:12 Volume:60 Page:2499-2507
ISSN:0013-9580
Container-title:Epilepsia
language:en
Short-container-title:Epilepsia

Author:

Bennett Oscar F.¹,Kanber Baris¹²³^ORCID,Hoskote Chandrashekar⁴,Cardoso M. Jorge⁵,Ourselin Sebastien⁵,Duncan John S.²³^ORCID,Winston Gavin P.²³⁶^ORCID

Affiliation:

1. Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London UK

2. Department of Clinical and Experimental Epilepsy Queen Square Institute of Neurology University College London London UK

3. MRI Unit Epilepsy Society Chalfont St Peter UK

4. Lysholm Department of Neuroradiology National Hospital for Neurology and Neurosurgery London UK

5. School of Biomedical Engineering & Imaging Sciences King’s College London London UK

6. Department of Medicine Division of Neurology Queen's University Kingston Ontario Canada

Funder

Engineering and Physical Sciences Research Council

Medical Research Council

Publisher

Wiley

Subject

Clinical Neurology,Neurology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.16380

Reference20 articles.

1. Early Identification of Refractory Epilepsy

2. Brain imaging in the assessment for epilepsy surgery

3. The long-term outcome of adult epilepsy surgery, patterns of seizure remission, and relapse: a cohort study

4. Preoperative MRI predicts outcome of temporal lobectomy: An actuarial analysis

5. Machine learning classification of mesial temporal sclerosis in epilepsy patients

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