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Automatic multiple sclerosis lesion segmentation using hybrid artificial neural networks
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-0442-3524
2016 (English)In: MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, Vol. 29Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2016. Vol. 29
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Medical Image Processing
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URN: urn:nbn:se:kth:diva-258881OAI: oai:DiVA.org:kth-258881DiVA, id: diva2:1350238
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-11

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CiteExportLink to record
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