Predicting respiratory motion using a novel patient specific dual deep recurrent neural networks.

Yoganathan, S A; Paloor, Satheesh; Torfeh, Tarraf; Aouadi, Souha; Hammoud, Rabih; Al-Hammadi, Noora

Yoganathan, S A; Paloor, Satheesh; Torfeh, Tarraf; Aouadi, Souha; Hammoud, Rabih; Al-Hammadi, Noora.

Afiliación

Yoganathan SA; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Paloor S; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Torfeh T; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Aouadi S; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Hammoud R; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Al-Hammadi N; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.

Biomed Phys Eng Express ; 8(6)2022 09 29.

Article en En | MEDLINE | ID: mdl-36130525

Asunto(s)

Algoritmos; Redes Neurales de la Computación; Predicción; Humanos; Movimiento (Física); Frecuencia Respiratoria

Palabras clave

GRU; LSTM; RNN; deep learning; latency; respiration

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Biomed Phys Eng Express Año: 2022 Tipo del documento: Article País de afiliación: Qatar Pais de publicación: Reino Unido

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