SwinTextUNet integrates CLIP text guidance into Swin U-Net via cross-attention and convolutional fusion, achieving 86.47% Dice and 78.2% IoU on QaTaCOV19 medical image segmentation.
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SwinTextUNet: Integrating CLIP-Based Text Guidance into Swin Transformer U-Nets for Medical Image Segmentation
SwinTextUNet integrates CLIP text guidance into Swin U-Net via cross-attention and convolutional fusion, achieving 86.47% Dice and 78.2% IoU on QaTaCOV19 medical image segmentation.