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A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.
Wanotayan, Rujira; Chousangsuntorn, Khaisang; Petisiwaveth, Phasit; Anuttra, Thunchanok; Lertchanyaphan, Waritsara; Jaikuna, Tanwiwat; Jangpatarapongsa, Kulachart; Uttayarat, Pimpon; Tongloy, Teerawat; Chousangsuntorn, Chousak; Boonsang, Siridech.
Afiliación
  • Wanotayan R; Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Chousangsuntorn K; Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Petisiwaveth P; Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Anuttra T; Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Lertchanyaphan W; Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Jaikuna T; Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Jangpatarapongsa K; Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand.
  • Uttayarat P; Nuclear Technology Research and Development Center, Thailand Institute of Nuclear Technology (Public Organization), Nakhon Nayok, Thailand.
  • Tongloy T; Center of Industrial Robot and Automation (CiRA), College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
  • Chousangsuntorn C; Department of Electrical Engineering, School of Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
  • Boonsang S; Department of Electrical Engineering, School of Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand. siridech.bo@kmitl.ac.th.
Sci Rep ; 12(1): 5527, 2022 04 01.
Article en En | MEDLINE | ID: mdl-35365702
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Tailandia
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Tailandia