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Open and reusable deep learning for pathology with WSInfer and QuPath.
Kaczmarzyk, Jakub R; O'Callaghan, Alan; Inglis, Fiona; Gat, Swarad; Kurc, Tahsin; Gupta, Rajarsi; Bremer, Erich; Bankhead, Peter; Saltz, Joel H.
Afiliação
  • Kaczmarzyk JR; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA. jakub.kaczmarzyk@stonybrookmedicine.edu.
  • O'Callaghan A; Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
  • Inglis F; Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
  • Gat S; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
  • Kurc T; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
  • Gupta R; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
  • Bremer E; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
  • Bankhead P; Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
  • Saltz JH; Edinburgh Pathology and CRUK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
NPJ Precis Oncol ; 8(1): 9, 2024 Jan 10.
Article em En | MEDLINE | ID: mdl-38200147
ABSTRACT
Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: NPJ Precis Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: NPJ Precis Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos