FedHarmony: Unlearning Scanner Bias with Distributed Data. (arXiv:2205.15970v1 [cs.LG])

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
scanner and acquisition differences - the harmonisation problem - and second,
data privacy concerns due to the inherently personal nature of medical imaging
data, meaning that sharing them across sites may risk violation of privacy
laws. To overcome …

arxiv bias data distributed distributed data