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粗读DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
2022-06-27 08:12:00 【格里芬阀门工】
本文的用了双胞胎网络，通过不同的patch_size，关注不同尺寸的信息，然后利用TIF模块进行融合。
所以核心创新点为两点：第一，通过不同的patch_size关注不同尺度的信息，第二，通过TIF融合信息。
TIF模块如图所示，就是把对方的特征图进行平均池化，然后作为加到对方拉平后的特征图尾部，进行transformer计算
同时，swin transformer部分如上图所示，attention模块不再是关注全局，而是分两步，每一步每个patch都只考虑周围的几个patch进行计算，SW-MSA的区别是会对图像整体往左上移动，消失的部分从右边和下面冒出来，然后计算MSA。具体操作见这篇文章：SW-MSA
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