  Self-supervised learning methods have witnessed a recent surge of interest
after proving successful in multiple application fields. In this work, we
leverage these techniques, and we propose 3D versions for five different
self-supervised methods, in the form of proxy tasks. Our methods facilitate
neural network feature learning from unlabeled 3D images, aiming to reduce the
required cost for expert annotation. The developed algorithms are 3D
Contrastive Predictive Coding, 3D Rotation prediction, 3D Jigsaw puzzles,
Relative 3D patch location, and 3D Exemplar networks. Our experiments show that
pretraining models with our 3D tasks yields more powerful semantic
representations, and enables solving downstream tasks more accurately and
efficiently, compared to training the models from scratch and to pretraining
them on 2D slices. We demonstrate the effectiveness of our methods on three
downstream tasks from the medical imaging domain: i) Brain Tumor Segmentation
from 3D MRI, ii) Pancreas Tumor Segmentation from 3D CT, and iii) Diabetic
Retinopathy Detection from 2D Fundus images. In each task, we assess the gains
in data-efficiency, performance, and speed of convergence. Interestingly, we
also find gains when transferring the learned representations, by our methods,
from a large unlabeled 3D corpus to a small downstream-specific dataset. We
achieve results competitive to state-of-the-art solutions at a fraction of the
computational expense. We publish our implementations for the developed
algorithms (both 3D and 2D versions) as an open-source library, in an effort to
allow other researchers to apply and extend our methods on their datasets.
