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  "author": "Olivier Petit",
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    {
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      "extra_author": "Nicolas Thome",
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    },
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      "extra_author": "Clement Rambour",
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      "extra_author": "Clement Rambour",
            
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Luc Soler",
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      "extra_author": "Luc Soler",
            
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      "orcid": ""
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      "description": "",
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      "description": "",
            
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      "name": "medical-image-segmentation",
            
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      "title": "Medical Image Segmentation"
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      "title": "Medical Image Segmentation"
            
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    }
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transformer--self-and-cross-attention-for-medical-image-segmentation",
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transformer--self-and-cross-attention-for-medical-image-segmentation",
            
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  "notes": "Medical image segmentation remains particularly 
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  "notes": "Medical image segmentation remains particularly 
            
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challenging for complex and low-contrast anatomical structures. In 
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challenging for complex and low-contrast anatomical structures. In 
            
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this paper, we introduce the U-Transformer network, which combines a 
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this paper, we introduce the U-Transformer network, which combines a 
            
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U-shaped architecture for image segmentation with self- and 
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U-shaped architecture for image segmentation with self- and 
            
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cross-attention from Transformers.",
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cross-attention from Transformers.",
            
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      "name": "Self-attention",
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      "name": "Self-attention",
            
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  "title": "U-Transformer: Self and Cross Attention for Medical Image 
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  "title": "U-Transformer: Self and Cross Attention for Medical Image 
            
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Segmentation",
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Segmentation",
            
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}