FedHarmony: unlearning scanner bias with distributed data

The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an increase in undesirable non-biological variance due to s...

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Detalles Bibliográficos
Autores principales: Dinsdale, N, Jenkinson, M, Namburete, A
Formato: Conference item
Lenguaje:English
Publicado: Springer 2022