![]() ![]() 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.īiomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. ![]() USST has two obvious merits compared to current dimension-specific SSL: (1) \textbf - can be transferred to various downstream tasks. 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. After that, the images are converted to a sequence regardless of their original dimensions. The SPE layer switches to either 2D or 3D patch embedding depending on the input dimension. 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. ![]() 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. 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. It is essential for medical image analysis that is generally known for its lack of annotations. Self-supervised learning (SSL) opens up huge opportunities for better utilizing unlabeled data. ![]()
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