用于胸部CT分割预训练的组织对比半掩码自编码器
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT
AI总结：
针对现有掩码图像建模应用于胸部CT的特征学习低效与上下游迁移差问题，提出组织对比半掩码自编码器TCS-MAE，通过组织级掩码重建和双自编码器对比学习，有效提升多类胸部CT分割任务性能。
摘要：
现有的掩码图像建模（MIM）依赖基于空间块的掩码-重建策略从无标签图像中感知物体特征，应用于胸部CT时可能面临两个局限：1）CT图像中复杂的解剖细节导致特征学习效率低下；2）上游与下游模型的输入差异造成知识迁移效果不佳。为解决这些问题，我们提出一种名为Tissue-Contrastive Semi-Masked Autoencoder（TCS-MAE，组织对比半掩码自编码器）的新型MIM方法，用于胸部CT图像建模。该方法有两项创新设计：1）基于组织的掩码-重建策略，以捕捉更细粒度的解剖特征；2）双自编码器架构，在掩码视图与原始图像视图之间引入对比学习，弥合上下游模型的差距。为验证方法有效性，我们在肺炎、纵隔肿瘤及多种器官分割任务上，系统对比了代表性的对比式、生成式及混合式自监督学习方法。结果表明，与现有方法相比，TCS-MAE能更有效地学习组织感知表征，从而显著提升所有任务的分割性能。
英文摘要：
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.
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