Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI

doi:doi:10.57702/qme2glxb

Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analysis due to their prominent performance.

BibTex:

@dataset{Jiahao_Huang_and_Xiaodan_Xing_and_Zhifan_Gao_and_Guang_Yang_2024,
    abstract = {Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analysis due to their prominent performance.},
    author = {Jiahao Huang and Xiaodan Xing and Zhifan Gao and Guang Yang},
    doi = {10.57702/qme2glxb},
    institution = {No Organization},
    keyword = {'Deep Learning', 'MRI Reconstruction', 'Medical Image Analysis', 'Transformer'},
    month = {dec},
    publisher = {TIB},
    title = {Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI},
    url = {https://service.tib.eu/ldmservice/dataset/swin-deformable-attention-u-net-transformer--sdaut--for-explainable-fast-mri},
    year = {2024}
}