Shypd.aiThe largest open index of AI toolsUNETR++ is an open-source tool for efficient and accurate 3D medical image segmentation. It introduces a novel efficient paired attention (EPA) block for high-quality segmentation masks and improved efficiency.
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UNETR++ is an open-source tool designed for efficient and accurate 3D medical image segmentation, developed by researchers from Mohamed Bin Zayed University of Artificial Intelligence, University of California Merced, Google Research, and Linkoping University. It addresses the computational bottleneck of traditional self-attention mechanisms in volumetric medical imaging by introducing a novel efficient paired attention (EPA) block. This block efficiently learns spatial and channel-wise discriminative features with linear complexity, reducing parameters, compute cost, and inference speed. The tool has been extensively evaluated on five benchmarks, including Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, demonstrating state-of-the-art performance with significant efficiency gains. It is available in Keras 3 as part of the AI Toolkit for Healthcare Imaging.
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