USEMA is a hybrid UNet architecture merging CNNs with scalable Mamba-like attention (SEMA) that achieves better efficiency than transformers and superior segmentation accuracy than pure CNN or Mamba models across medical imaging modalities.
In: Proceed- ings of the AAAI conference on artificial intelligence
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USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation
USEMA is a hybrid UNet architecture merging CNNs with scalable Mamba-like attention (SEMA) that achieves better efficiency than transformers and superior segmentation accuracy than pure CNN or Mamba models across medical imaging modalities.