USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation · DeepSignal
USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation arXiv cs.CV · Elisha Dayag, Nhat Thanh Tran, Jack Xin 4d ago · · · · USEMA introduces a hybrid UNet architecture combining CNNs with scalable Mamba-like attention for efficient medical image segmentation.
Key Points SEMA reduces computational complexity in medical image segmentation. Hybrid architecture enhances local feature extraction with attention. Experiments show improved efficiency and segmentation performance. Reader Mode is being prepared.
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Low signal — niche or repeat coverage.
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Source authority 20% 78
Community heat 20% 0
Technical impact 30% 33
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≥75 high · 50–74 medium · <50 low
Why Featured
USEMA's innovative architecture enhances medical image segmentation efficiency, signaling a significant advancement for developers, PMs, and investors in healthcare AI applications.