Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.

Ho, David Joon; Chui, M Herman; Vanderbilt, Chad M; Jung, Jiwon; Robson, Mark E; Park, Chan-Sik; Roh, Jin; Fuchs, Thomas J

Ho, David Joon; Chui, M Herman; Vanderbilt, Chad M; Jung, Jiwon; Robson, Mark E; Park, Chan-Sik; Roh, Jin; Fuchs, Thomas J.

Afiliación

Ho DJ; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Chui MH; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Vanderbilt CM; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Jung J; Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Robson ME; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Park CS; Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Roh J; Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
Fuchs TJ; Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

J Pathol Inform ; 14: 100160, 2023.

Article en En | MEDLINE | ID: mdl-36536772

Palabras clave

Annotation; Computational pathology; Deep learning; Ovarian cancer; Segmentation

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: J Pathol Inform Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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