  In this paper, we target self-supervised representation learning for
zero-shot tumor segmentation. We make the following contributions: First, we
advocate a zero-shot setting, where models from pre-training should be directly
applicable for the downstream task, without using any manual annotations.
Second, we take inspiration from "layer-decomposition", and innovate on the
training regime with simulated tumor data. Third, we conduct extensive ablation
studies to analyse the critical components in data simulation, and validate the
necessity of different proxy tasks. We demonstrate that, with sufficient
texture randomization in simulation, model trained on synthetic data can
effortlessly generalise to segment real tumor data. Forth, our approach
achieves superior results for zero-shot tumor segmentation on different
downstream datasets, BraTS2018 for brain tumor segmentation and LiTS2017 for
liver tumor segmentation. While evaluating the model transferability for tumor
segmentation under a low-annotation regime, the proposed approach also
outperforms all existing self-supervised approaches, opening up the usage of
self-supervised learning in practical scenarios.
