This paper concerns how machine learning explainability advances understanding of self-supervised learning for ultrasound video. We define the explainability as capturing anatomy-aware knowledge and propose a new set of quantitative metrics to evaluate explainability. We validate our proposed explainability approach on medical fetal ultrasound video self-supervised learning and demonstrate how it can guide the choice of self-supervised learning method. Our approach is attractive as it reveals biologically meaningful patterns which may instil human (clinician) trust in the trained model.
Kangning Zhang (University of Oxford)
Jianbo Jiao (University of Oxford)
Alison Noble (University of Oxford)
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