  Existing Masked Image Modeling (MIM) depends on a spatial patch-based
masking-reconstruction strategy to perceive objects'features from unlabeled
images, which may face two limitations when applied to chest CT: 1) inefficient
feature learning due to complex anatomical details presented in CT images, and
2) suboptimal knowledge transfer owing to input disparity between upstream and
downstream models. To address these issues, we propose a new MIM method named
Tissue-Contrastive Semi-Masked Autoencoder (TCS-MAE) for modeling chest CT
images. Our method has two novel designs: 1) a tissue-based
masking-reconstruction strategy to capture more fine-grained anatomical
features, and 2) a dual-AE architecture with contrastive learning between the
masked and original image views to bridge the gap of the upstream and
downstream models. To validate our method, we systematically investigate
representative contrastive, generative, and hybrid self-supervised learning
methods on top of tasks involving segmenting pneumonia, mediastinal tumors, and
various organs. The results demonstrate that, compared to existing methods, our
TCS-MAE more effectively learns tissue-aware representations, thereby
significantly enhancing segmentation performance across all tasks.
