Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation

@article{Zeng2022VideoTransUNetTB,
  title={Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation},
  author={Cheng Zeng and Xinyu Yang and Majid Mirmehdi and Alberto M. Gambaruto and Tilo Burghardt},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.08315}
}
We propose Video-TransUNet, a deep architecture for instance segmentation in medical CT videos constructed by integrating temporal feature blending into the TransUNet deep learning framework. In particular, our approach amalgamates strong frame representation via a ResNet CNN backbone, multi-frame feature blending via a Temporal Context Module (TCM), non-local attention via a Vision Transformer, and reconstructive capabilities for multiple targets via a UNet-based convolutional-deconvolutional… 

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