A U-Net-style architecture combining inception-style convolutions with a Mamba state-space block achieves state-of-the-art medical image segmentation at about one-fifth the GFLOPs of the previous best method.
Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer
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InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
A U-Net-style architecture combining inception-style convolutions with a Mamba state-space block achieves state-of-the-art medical image segmentation at about one-fifth the GFLOPs of the previous best method.