FedHarmony: unlearning scanner bias with distributed data

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...

Полное описание

Библиографические подробности
Главные авторы:	Dinsdale, N, Jenkinson, M, Namburete, A
Формат:	Conference item
Язык:	English
Опубликовано:	Springer 2022

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