The paper "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation" introduces a novel framework that combines the strengths of Transformers and U-Net for medical image segmentation. The authors address the limitations of U-Net, which struggle with long-range dependencies, and the limitations of Transformers, which lack fine-grained localization capabilities. TransUNet uses a hybrid CNN-Transformer architecture where the Transformer encodes tokenized image patches from a CNN feature map, capturing global contexts, while the decoder up samples these encoded features to combine with high-resolution CNN features for precise localization. The paper demonstrates that this approach achieves superior performance on various medical applications, including multi-organ and cardiac segmentation, compared to competing methods. The authors also provide detailed experimental results and ablation studies to validate the effectiveness of their proposed framework.The paper "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation" introduces a novel framework that combines the strengths of Transformers and U-Net for medical image segmentation. The authors address the limitations of U-Net, which struggle with long-range dependencies, and the limitations of Transformers, which lack fine-grained localization capabilities. TransUNet uses a hybrid CNN-Transformer architecture where the Transformer encodes tokenized image patches from a CNN feature map, capturing global contexts, while the decoder up samples these encoded features to combine with high-resolution CNN features for precise localization. The paper demonstrates that this approach achieves superior performance on various medical applications, including multi-organ and cardiac segmentation, compared to competing methods. The authors also provide detailed experimental results and ablation studies to validate the effectiveness of their proposed framework.