Your browser doesn't support javascript.
loading
Machine learning for medical imaging: methodological failures and recommendations for the future.
Varoquaux, Gaël; Cheplygina, Veronika.
Afiliación
  • Varoquaux G; INRIA, Versailles, France. gael.varoquaux@inria.fr.
  • Cheplygina V; McGill University, Montreal, Canada. gael.varoquaux@inria.fr.
NPJ Digit Med ; 5(1): 48, 2022 Apr 12.
Article en En | MEDLINE | ID: mdl-35413988
Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Reino Unido