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Robust, valid and interpretable deep learning for quantitative imaging (2022–2025)

Abstract:
One of the biggest challenges in employing artificial intelligence is the 'black-box' nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.
Grant type:
ARC Linkage Projects
Researchers:

  • Dr Steffen Bollmann

    Senior Research Fellow
    School of Electrical Engineering and Computer Science
    Faculty of Engineering, Architecture and Information Technology

  • Professor Markus Barth

    Professor
    School of Electrical Engineering and Computer Science
    Faculty of Engineering, Architecture and Information Technology

  • Dr Shakes Chandra

    Senior Lecturer
    School of Electrical Engineering and Computer Science
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council