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Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis.
Casella, Bruno; Riviera, Walter; Aldinucci, Marco; Menegaz, Gloria.
Afiliación
  • Casella B; Computer Science Department, University of Turin, 10149 Turin, Italy. Electronic address: bruno.casella@unito.it.
  • Riviera W; Computer Science Department, University of Verona, 37134 Verona, Italy.
  • Aldinucci M; Computer Science Department, University of Turin, 10149 Turin, Italy.
  • Menegaz G; Engineering for Innovation Medicine Department, University of Verona, 37134 Verona, Italy.
STAR Protoc ; 5(1): 102812, 2024 Mar 15.
Article en En | MEDLINE | ID: mdl-38180836
ABSTRACT
Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources. For complete details on the use and execution of this protocol, please refer to Casella et al.1.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: STAR Protoc Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: STAR Protoc Año: 2024 Tipo del documento: Article