  We propose iSegFormer, a memory-efficient transformer that combines a Swin
transformer with a lightweight multilayer perceptron (MLP) decoder. With the
efficient Swin transformer blocks for hierarchical self-attention and the
simple MLP decoder for aggregating both local and global attention, iSegFormer
learns powerful representations while achieving high computational
efficiencies. Specifically, we apply iSegFormer to interactive 3D medical image
segmentation.
