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UTNet: Hybrid Transformer for Medical Segmentation by daisy is a document available to read on EtoBox.

UTNet is a hybrid transformer-convolutional neural network for medical image segmentation. It integrates self-attention modules into a U-Net architecture to capture long-range dependencies at different scales. The paper proposes an efficient self-attention mechanism that reduces complexity from O(n2) to O(n), allowing self-attention to be applied to high-resolution feature maps. Evaluation on cardiac MRI data shows UTNet achieves superior segmentation performance compared to state-of-the-art approaches.

Author

daisy

Language

EN