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MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease.
Uegami, Wataru; Bychkov, Andrey; Ozasa, Mutsumi; Uehara, Kazuki; Kataoka, Kensuke; Johkoh, Takeshi; Kondoh, Yasuhiro; Sakanashi, Hidenori; Fukuoka, Junya.
Afiliação
  • Uegami W; Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Bychkov A; Department of Pathology, Kameda Medical Center, Kamogawa, Japan.
  • Ozasa M; Department of Pathology, Kameda Medical Center, Kamogawa, Japan.
  • Uehara K; Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Kataoka K; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan.
  • Johkoh T; Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan.
  • Kondoh Y; Department of Radiology, Kansai Rosai Hospital, Amagasaki, Hyogo, Japan.
  • Sakanashi H; Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan.
  • Fukuoka J; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan.
Mod Pathol ; 35(8): 1083-1091, 2022 08.
Article em En | MEDLINE | ID: mdl-35197560
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Pulmonares Intersticiais / Fibrose Pulmonar Idiopática / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Mod Pathol Assunto da revista: PATOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão
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: Doenças Pulmonares Intersticiais / Fibrose Pulmonar Idiopática / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Mod Pathol Assunto da revista: PATOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão