Computer Vision News - October 2022

58 Poster Presentation When investigating biological problems of interest, combining data across multiple sites and scanners is necessary to increase statistical power and the breadth of biological variability . However, doing this presents two problems: the harmonization problem and data privacy concerns. The harmonization problemoccurs because different scanners give different signals. The same subject acquired on different scanners will look subtly different due to the MRI scanner itself, rather than anything interesting in the person’s biology. When data is combined across scanners, this will increase noise . Data privacy is an issue because medical imaging data is inherently personal information, sosharingthisacrosssitescould be a contravention of privacy legislation. “Our approach is trying to overcome these Nicola Dinsdale is a postdoctoral research associate at the University of Oxford under the supervision of Ana Namburete. Her paper proposes a novel solution to the MRI data harmonization problem in a distributed setting. She speaks to us ahead of her poster this afternoon. FEDHARMONY: UNLEARNING SCANNER BIAS WITH DISTRIBUTED DATA Nicola Dinsdale BEST OF MICCAI