BTSwin-Unet: 3D U-shaped Symmetrical Swin Transformer-based Network for Brain Tumor Segmentation with Self-supervised Pre-training

作者：Liang, Junjie; Yang, Cihui^*; Zhong, Jingting; Ye, Xiaoli

来源：NEURAL PROCESSING LETTERS, 2023, 55(4): 3695-3713.

DOI：10.1007/s11063-022-10919-1

• 3D Swin Transformer
• Unet
• Tumor segmentation
• Self-supervised learning

Medical image automatic segmentation plays an important role in Computer-Aided Diagnosis system. Although convolution-based network has achieved great performance in medical image segmentation, it has limitations in modeling long-range contextual interactions and spatial dependencies. Due to the powerful ability of long-range information interaction of Vision Transformer, Vision Transformer have achieved advanced performance in several downstream tasks via self-supervised learning. In this paper, motivative by Swin Transformer, we proposed BTSwin-Unet, which is a 3D U-shaped symmetrical Swin Transformer-based network for brain tumor segmentation. Moreover, we construct a self-supervised learning framework to pre-train the model encoder through the reconstruction task. Extensive experiments on tumor segmentation tasks validated the performance of our proposed model, and our results consistently demonstrate favorable benchmarks.

• 单位
  南昌航空大学