The Vision Transformer (ViT) has demonstrated remarkable performance in
Self-Supervised Learning (SSL) for 3D medical image analysis. Mask AutoEncoder
(MAE) for feature pre-training can further unleash the potential of ViT on
various medical vision tasks. However, due to large spatial sizes with much
higher dimensions of 3D medical images, the lack of hierarchical design for MAE
may hinder the performance of downstream tasks. In this paper, we propose a
novel \textit{Mask in Mask (MiM)} pre-training framework for 3D medical images,
which aims to advance MAE by learning discriminative representation from
hierarchical visual tokens across varying scales. We introduce multiple levels
of granularity for masked inputs from the volume, which are then reconstructed
simultaneously ranging at both fine and coarse levels. Additionally, a
cross-level alignment mechanism is applied to adjacent level volumes to enforce
anatomical similarity hierarchically. Furthermore, we adopt a hybrid backbone
to enhance the hierarchical representation learning efficiently during the
pre-training. MiM was pre-trained on a large scale of available 3D volumetric
images, \textit{i.e.,} Computed Tomography (CT) images containing various body
parts. Extensive experiments on thirteen public datasets demonstrate the
superiority of MiM over other SSL methods in organ/lesion/tumor segmentation
and disease classification. We further scale up the MiM to large pre-training
datasets with more than 10k volumes, showing that large-scale pre-training can
further enhance the performance of downstream tasks. The improvement also
concluded that the research community should pay more attention to the scale of
the pre-training dataset towards the healthcare foundation model for 3D medical
images.

提出一种用于 3D 医学图像的新型预训练框架 “Mask in Mask（MiM）”，通过学习来自不同尺度的分层视觉标记的辨别性表示，在器官 / 病变 / 肿瘤分割和疾病分类等任务中展现出 MiM 相对于其他自我监督学习方法的优越性能，此外，大规模预训练数据集的扩展进一步提升了 MiM 在下游任务中的表现。