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...

書誌詳細
主要な著者:	Dinsdale, N, Jenkinson, M, Namburete, A
フォーマット:	Conference item
言語:	English
出版事項:	Springer 2022

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