Hui Xue

Principal Researcher

出版物

表示方法:

- SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation
  
  Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, A. Campbell-Washburn, Charlotte Manisty, James C. Moon, T. Treibel, Peter Kellman, Michael Hansen
  
  Radiology: Artificial Intelligence | October 2025, 巻7(6)
  
  Publication Preprint プロジェクト
- Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging
  
  Hui Xue, Sarah M. Hooper, Rhodri H. Davies, T. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, A. Campbell-Washburn, Peter Kellman, Michael Hansen
  
  April 2025
  
  Publication プロジェクト
- Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation
  
  Hui Xue
  
  Scientific Reports | March 2025, 巻14
  
  Publication
- A Study on Context Length and Efficient Transformers for Biomedical Image Analysis
  
  Sarah Hooper, Hui Xue
  
  Machine Learning for Health (ML4H) 2024 | November 2024
  
  Author's Version
- Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy
  
  Hui Xue
  
  ISMRM | May 2024
  
  Publication Publication
- Inline AI: Open-source Deep Learning Inference for Cardiac MR
  
  Hui Xue
  
  April 2024
  
  Publication Publication
- Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR
  
  Hui Xue
  
  Journal of Cardiovascular Magnetic Resonance | February 2024, 巻26
  
  Publication Publication

- SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation
  
  Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, A. Campbell-Washburn, Charlotte Manisty, James C. Moon, T. Treibel, Peter Kellman, Michael Hansen
  
  Radiology: Artificial Intelligence | October 2025, 巻7(6)
  
  Publication Preprint プロジェクト
- Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging
  
  Hui Xue, Sarah M. Hooper, Rhodri H. Davies, T. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, A. Campbell-Washburn, Peter Kellman, Michael Hansen
  
  April 2025
  
  Publication プロジェクト
- Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation
  
  Hui Xue
  
  Scientific Reports | March 2025, 巻14
  
  Publication
- A Study on Context Length and Efficient Transformers for Biomedical Image Analysis
  
  Sarah Hooper, Hui Xue
  
  Machine Learning for Health (ML4H) 2024 | November 2024
  
  Author's Version
- Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy
  
  Hui Xue
  
  ISMRM | May 2024
  
  Publication Publication
- Inline AI: Open-source Deep Learning Inference for Cardiac MR
  
  Hui Xue
  
  April 2024
  
  Publication Publication
- Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR
  
  Hui Xue
  
  Journal of Cardiovascular Magnetic Resonance | February 2024, 巻26
  
  Publication Publication
- SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation
  
  Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, A. Campbell-Washburn, Charlotte Manisty, James C. Moon, T. Treibel, Peter Kellman, Michael Hansen
  
  Radiology: Artificial Intelligence | October 2025, 巻7(6)
  
  Publication Preprint プロジェクト
- Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging
  
  Hui Xue, Sarah M. Hooper, Rhodri H. Davies, T. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, A. Campbell-Washburn, Peter Kellman, Michael Hansen
  
  April 2025
  
  Publication プロジェクト
- SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation
  
  Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, A. Campbell-Washburn, Charlotte Manisty, James C. Moon, T. Treibel, Peter Kellman, Michael Hansen
  
  Radiology: Artificial Intelligence | October 2025, 巻7(6)
  
  Publication Preprint プロジェクト
- Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging
  
  Hui Xue, Sarah M. Hooper, Rhodri H. Davies, T. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, A. Campbell-Washburn, Peter Kellman, Michael Hansen
  
  April 2025
  
  Publication プロジェクト
- A Study on Context Length and Efficient Transformers for Biomedical Image Analysis
  
  Sarah Hooper, Hui Xue
  
  Machine Learning for Health (ML4H) 2024 | November 2024
  
  Author's Version
- Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy
  
  Hui Xue
  
  ISMRM | May 2024
  
  Publication Publication
- Inline AI: Open-source Deep Learning Inference for Cardiac MR
  
  Hui Xue
  
  April 2024
  
  Publication Publication
- Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR
  
  Hui Xue
  
  Journal of Cardiovascular Magnetic Resonance | February 2024, 巻26
  
  Publication Publication
- Inline AI: Open-source Deep Learning Inference for Cardiac MR
  
  Hui Xue
  
  April 2024
  
  Publication Publication

- Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging
  
  Hui Xue, Sarah M. Hooper, Rhodri H. Davies, T. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, A. Campbell-Washburn, Peter Kellman, Michael Hansen
  
  April 2025
  
  Publication プロジェクト
- SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation
  
  Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, A. Campbell-Washburn, Charlotte Manisty, James C. Moon, T. Treibel, Peter Kellman, Michael Hansen
  
  Radiology: Artificial Intelligence | October 2025, 巻7(6)
  
  Publication Preprint プロジェクト
- Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation
  
  Hui Xue
  
  Scientific Reports | March 2025, 巻14
  
  Publication
- Inline AI: Open-source Deep Learning Inference for Cardiac MR
  
  Hui Xue
  
  April 2024
  
  Publication Publication
- Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR
  
  Hui Xue
  
  Journal of Cardiovascular Magnetic Resonance | February 2024, 巻26
  
  Publication Publication
- A Study on Context Length and Efficient Transformers for Biomedical Image Analysis
  
  Sarah Hooper, Hui Xue
  
  Machine Learning for Health (ML4H) 2024 | November 2024
  
  Author's Version
- Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy
  
  Hui Xue
  
  ISMRM | May 2024
  
  Publication Publication

Hui Xue

出版物

SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

Inline AI: Open-source Deep Learning Inference for Cardiac MR

Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR

SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

Inline AI: Open-source Deep Learning Inference for Cardiac MR

Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR

SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging

SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

Inline AI: Open-source Deep Learning Inference for Cardiac MR

Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR

Inline AI: Open-source Deep Learning Inference for Cardiac MR

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR<<1 Imaging

SNRAware: Improved Deep Learning MRI Denoising with Signal-to-Noise Ratio Unit Training and G-factor Map Augmentation

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Inline AI: Open-source Deep Learning Inference for Cardiac MR

Characterizing the Signal-to-noise Ratio and Spatio-temporal Resolution of an Imaging Transformer Model for CMR

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

お問い合わせ Hui Xue

Microsoft Health Futures