Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues <i>ex vivo</i> using deep learning and multispectral imaging.

Liu, George S; Shenson, Jared A; Farrell, Joyce E; Blevins, Nikolas H

Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging.

Liu, George S; Shenson, Jared A; Farrell, Joyce E; Blevins, Nikolas H.

Afiliación

Liu GS; Stanford University, Department of Otolaryngology - Head and Neck Surgery, Palo Alto, California, United States.
Shenson JA; Stanford University, Department of Otolaryngology - Head and Neck Surgery, Palo Alto, California, United States.
Farrell JE; Stanford University, Department of Electrical Engineering, Stanford, California, United States.
Blevins NH; Stanford University, Department of Otolaryngology - Head and Neck Surgery, Palo Alto, California, United States.

J Biomed Opt ; 28(1): 016004, 2023 01.

Article en En | MEDLINE | ID: mdl-36726664

Asunto(s)

Aprendizaje Profundo; Humanos; Relación Señal-Ruido; Redes Neurales de la Computación; Diagnóstico por Imagen

Palabras clave

classification; lasers; machine learning; multispectral imaging; sensor; tissue

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

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