由于现实世界中可用于训练数据驱动深度学习模型的三维医学数据集的注释数量有限，迁移学习在医学图像分析中引起了广泛关注。我们提出了一种新颖的迁移学习框架——医学变换器，能够将三维体积图像高效地建模为二维图像切片的序列。为了改进三维形式中的高层表示并增强空间关系，我们采用了一种多视图方法，利用三维体积的三个平面的信息，并提供了参数高效的训练。为了构建一个适用于各种任务的源模型，我们使用自监督学习（SSL）进行预训练，以蒙面编码向量预测作为代理任务，在大规模正常健康脑磁共振成像（MRI）数据集上进行。我们的预训练模型在三个下游任务上进行了评估：1）脑疾病诊断；2）脑龄预测；3）脑肿瘤分割，这些任务在脑MRI研究中被广泛研究。实验结果表明，我们的医学变换器在分类和回归任务中能够将参数数量高效地减少约92％，在分割任务中减少97％，并且在仅使用部分训练样本的情况下也能够取得良好的性能，优于最先进的迁移学习方法。

Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer, a novel transfer learning framework that effectively models 3-D volumetric images as a sequence of 2-D image slices. To improve the high-level representation in 3-D-form empowering spatial relations, we use a multiview approach that leverages information from three planes of the 3-D volume, while providing parameter-efficient training. For building a source model generally applicable to various tasks, we pretrain the model using self-supervised learning (SSL) for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset. Our pretrained model is evaluated on three downstream tasks: 1) brain disease diagnosis; 2) brain age prediction; and 3) brain tumor segmentation, which are widely studied in brain MRI research. Experimental results demonstrate that our Medical Transformer outperforms the state-of-the-art (SOTA) transfer learning methods, efficiently reducing the number of parameters by up to approximately 92% for classification and regression tasks and 97% for segmentation task, and it also achieves good performance in scenarios where only partial training samples are used.