  Self-supervised learning methods can be used to learn meaningful
representations from unlabeled data that can be transferred to supervised
downstream tasks to reduce the need for labeled data. In this paper, we propose
a 3D self-supervised method that is based on the contrastive (SimCLR) method.
Additionally, we show that employing Bayesian neural networks (with Monte-Carlo
Dropout) during the inference phase can further enhance the results on the
downstream tasks. We showcase our models on two medical imaging segmentation
tasks: i) Brain Tumor Segmentation from 3D MRI, ii) Pancreas Tumor Segmentation
from 3D CT. Our experimental results demonstrate the benefits of our proposed
methods in both downstream data-efficiency and performance.
