A semi-supervised learning-based quality evaluation system for digital chest radiographs.

Wei, Shuoyang; Qiu, Rui; Pu, Yanheng; Hu, Ankang; Niu, Yantao; Wu, Zhen; Zhang, Hui; Li, Junli

Wei, Shuoyang; Qiu, Rui; Pu, Yanheng; Hu, Ankang; Niu, Yantao; Wu, Zhen; Zhang, Hui; Li, Junli.

Afiliación

Wei S; Department of Engineering Physics, Tsinghua University, Beijing, China.
Qiu R; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
Pu Y; Department of Radiotherapy, Peking Union Medical College Hospital, Beijing, China.
Hu A; Department of Engineering Physics, Tsinghua University, Beijing, China.
Niu Y; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
Wu Z; Department of Engineering Physics, Tsinghua University, Beijing, China.
Zhang H; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
Li J; Department of Engineering Physics, Tsinghua University, Beijing, China.

Med Phys ; 50(11): 6789-6800, 2023 Nov.

Article en En | MEDLINE | ID: mdl-37543992

Asunto(s)

Aprendizaje Profundo; Cuerpos Extraños; Humanos; Radiografía; Redes Neurales de la Computación; Aprendizaje Automático Supervisado

Palabras clave

artificial intelligence; digital radiograph; quality evaluation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cuerpos Extraños / Aprendizaje Profundo Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Med Phys Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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