  Image representations are commonly learned from class labels, which are a
simplistic approximation of human image understanding. In this paper we
demonstrate that transferable representations of images can be learned without
manual annotations by modeling human visual attention. The basis of our
analyses is a unique gaze tracking dataset of sonographers performing routine
clinical fetal anomaly screenings. Models of sonographer visual attention are
learned by training a convolutional neural network (CNN) to predict gaze on
ultrasound video frames through visual saliency prediction or gaze-point
regression. We evaluate the transferability of the learned representations to
the task of ultrasound standard plane detection in two contexts. Firstly, we
perform transfer learning by fine-tuning the CNN with a limited number of
labeled standard plane images. We find that fine-tuning the saliency predictor
is superior to training from random initialization, with an average F1-score
improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we
train a simple softmax regression on the feature activations of each CNN layer
in order to evaluate the representations independently of transfer learning
hyper-parameters. We find that the attention models derive strong
representations, approaching the precision of a fully-supervised baseline model
for all but the last layer.
