Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches.

Ellis, Charles A; Miller, Robyn L; Calhoun, Vince D

Ellis, Charles A; Miller, Robyn L; Calhoun, Vince D.

Afiliación

Ellis CA; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, United States.
Miller RL; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, United States.
Calhoun VD; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, United States.

Inform Med Unlocked ; 372023.

Article en En | MEDLINE | ID: mdl-37035832

Palabras clave

Clinical decision support systems; Explainable artificial intelligence; Gradient-based explainability methods; Monte Carlo batch normalization; Monte Carlo dropout; Neuroimaging

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Inform Med Unlocked Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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