  Masked autoencoder (MAE) is a promising self-supervised pre-training
technique that can improve the representation learning of a neural network
without human intervention. However, applying MAE directly to volumetric
medical images poses two challenges: (i) a lack of global information that is
crucial for understanding the clinical context of the holistic data, (ii) no
guarantee of stabilizing the representations learned from randomly masked
inputs. To address these limitations, we propose the
\textbf{G}lobal-\textbf{L}ocal \textbf{M}asked \textbf{A}uto\textbf{E}ncoder
(GL-MAE), a simple yet effective self-supervised pre-training strategy. In
addition to reconstructing masked local views, as in previous methods, GL-MAE
incorporates global context learning by reconstructing masked global views.
Furthermore, a complete global view is integrated as an anchor to guide the
reconstruction and stabilize the learning process through global-to-global
consistency learning and global-to-local consistency learning. Finetuning
results on multiple datasets demonstrate the superiority of our method over
other state-of-the-art self-supervised algorithms, highlighting its
effectiveness on versatile volumetric medical image segmentation tasks, even
when annotations are scarce. Our codes and models will be released upon
acceptance.
