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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Dinsdale, N, Jenkinson, M, Namburete, A
Format: Conference item
Język:English
Wydane: Springer 2022

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