  Automatic and accurate tumor segmentation on medical images is in high demand
to assist physicians with diagnosis and treatment. However, it is difficult to
obtain massive amounts of annotated training data required by the deep-learning
models as the manual delineation process is often tedious and expertise
required. Although self-supervised learning (SSL) scheme has been widely
adopted to address this problem, most SSL methods focus only on global
structure information, ignoring the key distinguishing features of tumor
regions: local intensity variation and large size distribution. In this paper,
we propose Scale-Aware Restoration (SAR), a SSL method for 3D tumor
segmentation. Specifically, a novel proxy task, i.e. scale discrimination, is
formulated to pre-train the 3D neural network combined with the
self-restoration task. Thus, the pre-trained model learns multi-level local
representations through multi-scale inputs. Moreover, an adversarial learning
module is further introduced to learn modality invariant representations from
multiple unlabeled source datasets. We demonstrate the effectiveness of our
methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas
tumor segmentation. Compared with the state-of-the-art 3D SSL methods, our
proposed approach can significantly improve the segmentation accuracy. Besides,
we analyze its advantages from multiple perspectives such as data efficiency,
performance, and convergence speed.
