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Transformers outshine convolutional neural networks and recurrent neural networks in many applications from various domains, including natural language processing, image classification and medical ...

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image ...

TransUNet, a Transformers-based U-Net framework, achieves state-of-the-art performance in medical image segmentation applications. U-Net, the U-shaped convolutional neural network architecture, ...

Automatic medical image segmentation facilitates accurate computer-aided diagnosis and computer-assisted surgery. Current segmentation approaches usually use convolutional neural networks that ...

The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved outstanding success.