Training confounder-free deep learning models for medical applications

The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.

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Detalles Bibliográficos
Autores principales: Qingyu Zhao, Ehsan Adeli, Kilian M. Pohl
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Q
Acceso en línea:https://doaj.org/article/59077cc4c0464b168994c4db5c2b33d0
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