Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning

Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of human experts, in addition to requiring prohibitive a...

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Detalles Bibliográficos
Autores principales: Waldstein, Sebastian M., Seeböck, Philipp, Donner, René, Sadeghipour, Amir, Bogunović, Hrvoje, Osborne, Aaron, Schmidt-Erfurth, Ursula
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395081/
https://www.ncbi.nlm.nih.gov/pubmed/32737379
http://dx.doi.org/10.1038/s41598-020-69814-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395081/
https://www.ncbi.nlm.nih.gov/pubmed/32737379
http://dx.doi.org/10.1038/s41598-020-69814-1

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