DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.

Rajaraman, Sivaramakrishnan; Cohen, Gregg; Spear, Lillian; Folio, Les; Antani, Sameer

Rajaraman, Sivaramakrishnan; Cohen, Gregg; Spear, Lillian; Folio, Les; Antani, Sameer.

Afiliação

Rajaraman S; National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.
Cohen G; Clinical Center, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, United States of America.
Spear L; Clinical Center, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, United States of America.
Folio L; Clinical Center, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, United States of America.
Antani S; National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.

PLoS One ; 17(3): e0265691, 2022.

Article em En | MEDLINE | ID: mdl-35358235

Assuntos

COVID-19/diagnóstico por imagem; Pneumopatias/diagnóstico por imagem; Redes Neurais de Computação; Radiografia Torácica/métodos; Clavícula/diagnóstico por imagem; Humanos; Costelas/diagnóstico por imagem; Razão Sinal-Ruído

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiografia Torácica / Redes Neurais de Computação / COVID-19 / Pneumopatias Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: PLoS One Ano de publicação: 2022 Tipo de documento: Article

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