  The paradigm of self-supervision focuses on representation learning from raw
data without the need of labor-consuming annotations, which is the main
bottleneck of current data-driven methods. Self-supervision tasks are often
used to pre-train a neural network with a large amount of unlabeled data and
extract generic features of the dataset. The learned model is likely to contain
useful information which can be transferred to the downstream main task and
improve performance compared to random parameter initialization. In this paper,
we propose a new self-supervision task called source identification (SI), which
is inspired by the classic blind source separation problem. Synthetic images
are generated by fusing multiple source images and the network's task is to
reconstruct the original images, given the fused images. A proper understanding
of the image content is required to successfully solve the task. We validate
our method on two medical image segmentation tasks: brain tumor segmentation
and white matter hyperintensities segmentation. The results show that the
proposed SI task outperforms traditional self-supervision tasks for dense
predictions including inpainting, pixel shuffling, intensity shift, and
super-resolution. Among variations of the SI task fusing images of different
types, fusing images from different patients performs best.
