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...
| 主要な著者: | , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Springer
2022
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