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An appraisal of the performance of AI tools for chronic stroke lesion segmentation.
Ahmed, Ramsha; Al Shehhi, Aamna; Hassan, Bilal; Werghi, Naoufel; Seghier, Mohamed L.
Afiliación
  • Ahmed R; Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
  • Al Shehhi A; Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
  • Hassan B; Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
  • Werghi N; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
  • Seghier ML; Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. Electronic address: mseghier@gmail.com.
Comput Biol Med ; 164: 107302, 2023 09.
Article en En | MEDLINE | ID: mdl-37572443
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Accidente Cerebrovascular Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Accidente Cerebrovascular Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article