  Recent advances in deep learning have achieved promising performance for
medical image analysis, while in most cases ground-truth annotations from human
experts are necessary to train the deep model. In practice, such annotations
are expensive to collect and can be scarce for medical imaging applications.
Therefore, there is significant interest in learning representations from
unlabelled raw data. In this paper, we propose a self-supervised learning
approach to learn meaningful and transferable representations from medical
imaging video without any type of human annotation. We assume that in order to
learn such a representation, the model should identify anatomical structures
from the unlabelled data. Therefore we force the model to address anatomy-aware
tasks with free supervision from the data itself. Specifically, the model is
designed to correct the order of a reshuffled video clip and at the same time
predict the geometric transformation applied to the video clip. Experiments on
fetal ultrasound video show that the proposed approach can effectively learn
meaningful and strong representations, which transfer well to downstream tasks
like standard plane detection and saliency prediction.
