UNETR, or UNet Transformer, is a Transformer-based architecture for medical image segmentation that utilizes a pure transformer as the encoder to learn sequence representations of the input volume -- effectively capturing the global multi-scale information. The transformer encoder is directly connected to a decoder via skip connections at different resolutions like a U-Net to compute the final semantic segmentation output.
Source: UNETR: Transformers for 3D Medical Image SegmentationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 10 | 19.61% |
Image Segmentation | 9 | 17.65% |
Medical Image Segmentation | 7 | 13.73% |
Tumor Segmentation | 3 | 5.88% |
Self-Supervised Learning | 2 | 3.92% |
Decoder | 2 | 3.92% |
Instance Segmentation | 1 | 1.96% |
Zero-shot Generalization | 1 | 1.96% |
Zero Shot Segmentation | 1 | 1.96% |