Papers › iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

21 Dec 2021arXiv:2112.11325archive 2025-07-28

Qin Liu, Zhenlin Xu, Yining Jiao, Marc Niethammer

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.

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uncbiag/isegformer officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Decoder Image Segmentation Interactive Segmentation Medical Image Segmentation Semantic Segmentation

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Methods

Attention Dense Connections Layer Normalization Linear Layer Multi-Head Attention Residual Connection Softmax Stochastic Depth Swin Transformer

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