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Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.
Wang, Li; Ding, Wenlong; Mo, Yan; Shi, Dejun; Zhang, Shuo; Zhong, Lingshan; Wang, Kai; Wang, Jigang; Huang, Chencui; Zhang, Shu; Ye, Zhaoxiang; Shen, Jun; Xing, Zhiheng.
Afiliação
  • Wang L; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Ding W; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Mo Y; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
  • Shi D; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
  • Zhang S; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Zhong L; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Wang K; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Wang J; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
  • Huang C; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
  • Zhang S; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
  • Ye Z; National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China.
  • Shen J; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China. hhyy_shenjun@outlook.com.
  • Xing Z; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China. 18920696025@189.cn.
Eur J Nucl Med Mol Imaging ; 48(13): 4293-4306, 2021 12.
Article em En | MEDLINE | ID: mdl-34131803
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Aprendizado Profundo / Pneumopatias Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies Limite: Humans Idioma: En Revista: Eur J Nucl Med Mol Imaging Assunto da revista: MEDICINA NUCLEAR Ano de publicação: 2021 Tipo de documento: Article País de publicação: Alemanha
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Aprendizado Profundo / Pneumopatias Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies Limite: Humans Idioma: En Revista: Eur J Nucl Med Mol Imaging Assunto da revista: MEDICINA NUCLEAR Ano de publicação: 2021 Tipo de documento: Article País de publicação: Alemanha