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Deep-learning based brain tumor segmentation using quantitative MRI
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7582-1706
Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.ORCID iD: 0000-0002-8857-5698
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0555-8877
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.ORCID iD: 0000-0001-7061-7995
2022 (English)Conference paper, Poster (with or without abstract) (Refereed)
Place, publisher, year, edition, pages
2022.
Keywords [en]
Deep learning, Magnetic resonance imaging, MRI, brain tumor, segmentation
National Category
Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-182823OAI: oai:DiVA.org:liu-182823DiVA, id: diva2:1636814
Conference
Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM)
Available from: 2022-02-10 Created: 2022-02-10 Last updated: 2025-02-09Bibliographically approved

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Blystad, IdaEklund, Anders

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Tampu, Iulian EmilBlystad, IdaHaj-Hosseini, NedaEklund, Anders
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Division of Biomedical EngineeringCenter for Medical Image Science and Visualization (CMIV)Faculty of Science & EngineeringFaculty of Medicine and Health SciencesDepartment of Radiology in LinköpingDivision of Diagnostics and Specialist MedicineThe Division of Statistics and Machine Learning
Medical Imaging

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CiteExportLink to record
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