On the Relevance of Temporal Features for Medical Ultrasound Video
  Recognition
D. Hudson Smith John Paul Lineberger George H. Baker
Many medical ultrasound video recognition tasks involve identifying key
anatomical features regardless of when they appear in the video suggesting that
modeling such tasks may not benefit from temporal features. Correspondingly,
model architectures that exclude temporal features may have better sample
efficiency. We propose a novel multi-head attention architecture that
incorporates these hypotheses as inductive priors to achieve better sample
efficiency on common ultrasound tasks. We compare the performance of our
architecture to an efficient 3D CNN video recognition model in two settings:
one where we expect not to require temporal features and one where we do. In
the former setting, our model outperforms the 3D CNN - especially when we
artificially limit the training data. In the latter, the outcome reverses.
These results suggest that expressive time-independent models may be more
effective than state-of-the-art video recognition models for some common
ultrasound tasks in the low-data regime.