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...

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Príomhchruthaitheoirí:	Dinsdale, N, Jenkinson, M, Namburete, A
Formáid:	Conference item
Teanga:	English
Foilsithe / Cruthaithe:	Springer 2022

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