Unlearning Scanner Bias for MRI Harmonisation

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

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

    5 Citations (Scopus)

    Abstract

    Combining datasets is vital for increased statistical power, especially for neurological conditions where limited data is available. However, variance due to differences in acquisition protocol and hardware limits our ability to combine datasets. We propose an iterative training scheme based on domain adaptation techniques, aiming to create scanner-invariant features while simultaneously maintaining overall performance on the main task. We demonstrate this on age prediction, but expect that our proposed training scheme will be applicable to any feedforward network and classification or regression task. We show that not only can we harmonise three MRI datasets from different studies, but can also successfully adapt the training to work with very biased datasets. The training scheme should, therefore, be applicable to most real-world data scenarios, enabling harmonisation for the task of interest.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
    EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages369-378
    Number of pages10
    ISBN (Print)9783030597122
    DOIs
    Publication statusPublished or Issued - 2020
    Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
    Duration: 4 Oct 20208 Oct 2020

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12262 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
    CountryPeru
    CityLima
    Period4/10/208/10/20

    Keywords

    • Harmonisation
    • Joint domain adaptation
    • MRI

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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