A transformer backbone and corresponding training recipe, which can achieve top performances under different medical image segmentation scenarios, still needs to be developed. In this paper, we enhance the Swi-nUNETR with convolutions, which results in a surprisingly stronger backbone, the SwinUNETR-...
代码地址:monai.io/research/swin- 自监督预训练流程图总览 一、简介 为vision transformer 模型设计了一种适用于医学影像的自监督方法。主要的创新点为设计了一种混合自监督代理任务,包括旋转预测,实例对抗以及纹理填充 (inpainting)。方法的有效性是通过微调的效果来论证的,而不是自然图像中比较常用的 linear probing...
2) Transformer的局部感应偏置不足会影响对模糊边界等细节特征的分割能力。因此,要将Vision Transformer机制应用于医学图像分割领域,需要充分克服上述挑战。 宾夕法尼亚大学的Jayaram团队提出了可变形状混合Transformer(VSmTrans),它集成了自注意和卷积,可以从自注意机制中学习复杂关系,也可以从卷积中学习局部先验知识。具体...
Swin UNETR 模型体系结构由一个 Swin transformer 编码器组成,该编码器使用 3D 补丁,并通过不同分辨率的跳过连接连接到基于 CNN 的解码器 结论 Swin UNETR 体系结构在使用变压器的医疗成像方面提供了急需的突破。鉴于医学成像需要快速构建准确的模型, Swin UNETR 体系结构使数据科学家能够对大...
在UNETR中,Transformer块编码能捕获一致的全局表示的特征,并随后在基于CNN的解码器内跨各种分辨率进行整合。Zhou等人[31]提出了nnFormer,这是一种源自Swin-UNet[3]架构的方法。Wang等人[24]提出了TransBTS,该方法使用常规的卷积编码器-解码器架构和一个Transformer层作为瓶颈。
Swin Transformers 采用分层视觉 transformer ( ViT )进行非重叠窗口的局部自我注意计算。这打开了为大型公司创建医疗专用 ImageNet 的机会,消除了创建医疗AI模型需要大量高质量注释数据集的瓶颈。 与CNN 体系结构相比, ViT 在从未标记数据(数据集越大,预训练主干越强)进行全局和局部表示的自监督学习方面表现出非凡的能...
is a pure transformer network structure, where the encoder and decoders are composed of transformers. however, swin-unet is a model for 2d medical image segmentation, which is not applicable to voxel segmentation of 3d medical images unless a lot of additional work has been performed or some ...
Unlike the original version of Swin Transformer, the self-attention calculations in our model are performed in a 3D dimensional space, which can capture spatial dependencies of the medical image across 2D slices. In the self-attention branch, we divide the vector Xa into disjoint windows and ...
Paper tables with annotated results for Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis