Self-supervised learning (SSL) opens up huge opportunities for better utilizing unlabeled data. It is essential for medical image analysis that is generally known for its lack of annotations. However, when we attempt to use as many as possible unlabeled medical images in SSL, breaking the dimension barrier (\ie, making it possible to jointly use both 2D and 3D images) becomes a must. In this paper, we propose a Universal Self-Supervised Transformer (USST) framework based on the student-teacher paradigm, aiming to leverage a huge of unlabeled medical data with multiple dimensions to learn rich representations. To achieve this, we design a Pyramid Transformer U-Net (PTU) as the backbone, which is composed of switchable patch embedding (SPE) layers and Transformer layers. The SPE layer switches to either 2D or 3D patch embedding depending on the input dimension. After that, the images are converted to a sequence regardless of their original dimensions. The Transformer layer then models the long-term dependencies in a sequence-to-sequence manner, thus enabling USST to learn representations from both 2D and 3D images. USST has two obvious merits compared to current dimension-specific SSL: (1) \textbf{more effective} - can learn representations from more and diverse data; and (2) \textbf{more versatile} - can be transferred to various downstream tasks. The results show that USST provides promising results on six 2D/3D medical image classification and segmentation tasks, outperforming the supervised ImageNet pre-training and advanced SSL counterparts substantially.

本文提出了一个名为UniMiSS的医学自监督表示学习框架，使用复杂的pyramid U-like医学Transformer来处理2D和3D医学图像，并在各种下游任务中显示出优异的性能。

UniMiSS: 通过打破维度障碍实现通用医疗自监督学习