  Deep learning-based computer-aided diagnosis is gradually deployed to review
and analyze medical images. However, this paradigm is restricted in real-world
clinical applications due to the poor robustness and generalization. The issue
is more sinister with a lack of training data. In this paper, we address the
challenge from the transfer learning point of view. Different from the common
setting that transferring knowledge from the natural image domain to the
medical image domain, we find the knowledge from the same domain further boosts
the model robustness and generalization. Therefore, we propose a novel
two-stage framework for robust generalized medical image segmentation. Firstly,
an unsupervised tile-wise autoencoder pretraining architecture is proposed to
learn local and global knowledge. Secondly, the downstream segmentation model
coupled with an auxiliary reconstruction network is designed. The
reconstruction branch encourages the model to capture more general semantic
features. Experiments of lung segmentation on multi chest X-ray datasets are
conducted. Comprehensive results demonstrate the superior robustness of the
proposed framework to corruption and high generalization performance on unseen
datasets, especially under the scenario of the limited training data.
