  Auditory and visual signals usually present together and correlate with each
other, not only in natural environments but also in clinical settings. However,
the audio-visual modelling in the latter case can be more challenging, due to
the different sources of audio/video signals and the noise (both signal-level
and semantic-level) in auditory signals -- usually speech. In this paper, we
consider audio-visual modelling in a clinical setting, providing a solution to
learn medical representations that benefit various clinical tasks, without
human expert annotation. A simple yet effective multi-modal self-supervised
learning framework is proposed for this purpose. The proposed approach is able
to localise anatomical regions of interest during ultrasound imaging, with only
speech audio as a reference. Experimental evaluations on a large-scale clinical
multi-modal ultrasound video dataset show that the proposed self-supervised
method learns good transferable anatomical representations that boost the
performance of automated downstream clinical tasks, even outperforming
fully-supervised solutions.
