Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CT.

Sandfort, Veit; Yan, Ke; Graffy, Peter M; Pickhardt, Perry J; Summers, Ronald M

Sandfort, Veit; Yan, Ke; Graffy, Peter M; Pickhardt, Perry J; Summers, Ronald M.

Afiliación

Sandfort V; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (V.S., K.Y., R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.M.G., P.J.P.).
Yan K; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (V.S., K.Y., R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.M.G., P.J.P.).
Graffy PM; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (V.S., K.Y., R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.M.G., P.J.P.).
Pickhardt PJ; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (V.S., K.Y., R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.M.G., P.J.P.).
Summers RM; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (V.S., K.Y., R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.M.G., P.J.P.).

Radiol Artif Intell ; 3(4): e200218, 2021 Jul.

Article en En | MEDLINE | ID: mdl-34350410

Palabras clave

CT; Convolutional Neural Network (CNN); Deep Learning Algorithms; Machine Learning Algorithms; Segmentation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Revista: Radiol Artif Intell Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos

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