AI RESEARCH
USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation
arXiv CS.CV
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ArXi:2605.11131v1 Announce Type: new Accurate medical image segmentation is an integral part of the medical image analysis pipeline that requires the ability to merge local and global information. While vision transformers are able to capture global interactions using vanilla self-attention, their quadratic computational complexity in the input size remains a struggle for medical image segmentation tasks.