SFHarmony: source free domain adaptation for distributed neuroimaging analysis

Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the 'harmonisation problem'. Additionally, neuroimaging data is inherently personal in nature, leading to data privacy concerns when sharing the data. To overcome these barriers, we propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony. Through modelling the imaging features as a Gaussian Mixture Model and minimising an adapted Bhattacharyya distance between the source and target features, we can create a model that performs well for the target data whilst having a shared feature representation across the data domains, without needing access to the source data for adaptation or target labels. We demonstrate the performance of our method on simulated and real domain shifts, showing that the approach is applicable to classification, segmentation and regression tasks, requiring no changes to the algorithm. Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems. Our code is available at https://github.com/nkdinsdale/SFHarmony.

    Original languageEnglish
    Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages11460-11471
    Number of pages12
    ISBN (Electronic)9798350307184
    DOIs
    Publication statusPublished or Issued - 15 Jan 2024
    Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
    Duration: 2 Oct 20236 Oct 2023

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499

    Conference

    Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
    Country/TerritoryFrance
    CityParis
    Period2/10/236/10/23

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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