publications

2024

1. 

   Medical Multimodal Multitask Foundation Model for Lung Cancer Screening

   Chuang Niu, Qing Lyu, Christopher Carothers, and 6 more authors

   Accepted by Nature Communications, 2024

   Bib Code

   @article{M3FM,
     title = {Medical Multimodal Multitask Foundation Model for Lung Cancer Screening},
     author = {Niu, Chuang and Lyu, Qing and Carothers, Christopher and Kaviani, Parisa and Tan, Josh and Yan, Pingkun and Kalra, Mannudeep and Whitlow, Christopher and Wang, Ge},
     journal = {Accepted by Nature Communications},
     year = {2024},
     url = {https://arxiv.org/abs/2304.02649},
   }

2. 

   Development and Validation of a Dynamic-Template-Constrained Large Language Model for Generating Fully-Structured Radiology Reports

   Chuang Niu, Parisa Kaviani, Qing Lyu, and 3 more authors

   2024

   Bib Code

   @misc{llm,
     title = {Development and Validation of a Dynamic-Template-Constrained Large Language Model for Generating Fully-Structured Radiology Reports},
     author = {Niu, Chuang and Kaviani, Parisa and Lyu, Qing and Kalra, Mannudeep K. and Whitlow, Christopher T. and Wang, Ge},
     year = {2024},
     eprint = {2409.18319},
     archiveprefix = {arXiv},
     primaryclass = {cs.AI},
     url = {https://arxiv.org/abs/2409.18319},
   }

2023

1. 

   3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model

   Chuang Niu, Christopher Wiedeman, Mengzhou Li, and 2 more authors

   Fully3D, 2023

   Bib

   @article{niu2023ddpm,
     title = {3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model},
     author = {Niu, Chuang and Wiedeman, Christopher and Li, Mengzhou and Maltz, Jonathan and Wang, Ge},
     journal = {Fully3D},
     year = {2023},
   }

2. 

   Variable Discretization for Self-Supervised Learning

   Chuang Niu, Wenjun Xia, and Ge Wang

   In ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models, 2023

   Bib Code

   @inproceedings{niu2023,
     title = {Variable Discretization for Self-Supervised Learning},
     author = {Niu, Chuang and Xia, Wenjun and Wang, Ge},
     booktitle = {ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models},
     volume = {},
     pages = {},
     year = {2023},
     organization = {ICLR},
   }

2022

1. 

   X-ray dissectography enables stereotography

   Chuang Niu, and Ge Wang

   In 7th International Conference on Image Formation in X-Ray Computed Tomography, 2022

   Bib

   @inproceedings{niu2022x,
     title = {X-ray dissectography enables stereotography},
     author = {Niu, Chuang and Wang, Ge},
     booktitle = {7th International Conference on Image Formation in X-Ray Computed Tomography},
     volume = {12304},
     pages = {396--401},
     year = {2022},
     organization = {SPIE},
   }

2. 

   Unsupervised contrastive learning based transformer for lung nodule detection

   Chuang Niu, and Ge Wang

   Physics in Medicine & Biology, Oct 2022

   DOI Bib

   @article{Niu_2022,
     doi = {10.1088/1361-6560/ac92ba},
     url = {https://dx.doi.org/10.1088/1361-6560/ac92ba},
     year = {2022},
     month = oct,
     publisher = {IOP Publishing},
     volume = {67},
     number = {20},
     pages = {204001},
     author = {Niu, Chuang and Wang, Ge},
     title = {Unsupervised contrastive learning based transformer for lung nodule detection},
     journal = {Physics in Medicine & Biology},
   }

3. 

   Self-supervised dual-domain network for low-dose CT denoising

   Chuang Niu, Mengzhou Li, Xiaodong Guo, and 1 more author

   In Proc. of SPIE Vol, Oct 2022

   Bib

   @inproceedings{niu2022self,
     title = {Self-supervised dual-domain network for low-dose CT denoising},
     author = {Niu, Chuang and Li, Mengzhou and Guo, Xiaodong and Wang, Ge},
     booktitle = {Proc. of SPIE Vol},
     volume = {12242},
     pages = {122420H--1},
     year = {2022},
   }

4. 

   Multi-view x-ray dissectography improves nodule detection

   Chuang Niu, Giridhar Dasegowda, Pingkun Yan, and 2 more authors

   In Developments in X-Ray Tomography XIV, Oct 2022

   DOI Bib

   @inproceedings{10.1117/12.2637782,
     author = {Niu, Chuang and Dasegowda, Giridhar and Yan, Pingkun and Kalra, Mannudeep K. and Wang, Ge},
     title = {{Multi-view x-ray dissectography improves nodule detection}},
     volume = {12242},
     booktitle = {Developments in X-Ray Tomography XIV},
     editor = {M{\"u}ller, Bert and Wang, Ge},
     organization = {International Society for Optics and Photonics},
     publisher = {SPIE},
     pages = {1224217},
     keywords = {Lung nodule detection, x-ray imaging},
     year = {2022},
     doi = {10.1117/12.2637782},
     url = {https://doi.org/10.1117/12.2637782},
   }

5. 

   Home: high-order mixed-moment-based embedding for representation learning

   Chuang Niu, and Ge Wang

   arXiv preprint arXiv:2207.07743, Oct 2022

   Bib

   @article{niu2022home,
     title = {Home: high-order mixed-moment-based embedding for representation learning},
     author = {Niu, Chuang and Wang, Ge},
     journal = {arXiv preprint arXiv:2207.07743},
     year = {2022},
   }

6. 

   Self-supervised representation learning with MUlti-Segmental Informational Coding (MUSIC)

   Chuang Niu, and Ge Wang

   arXiv preprint arXiv:2206.06461, Oct 2022

   Bib

   @article{niu2022music,
     title = {Self-supervised representation learning with {MU}lti-{S}egmental {I}nformational {C}oding ({MUSIC})},
     author = {Niu, Chuang and Wang, Ge},
     journal = {arXiv preprint arXiv:2206.06461},
     year = {2022},
   }

7. 

   SPICE: Semantic Pseudo-labeling for Image Clustering

   Chuang Niu, Hongming Shan, and Ge Wang

   TIP, Oct 2022

   Bib Code

   @article{niu2021spice,
     title = {{SPICE}: Semantic {P}seudo-labeling for {I}mage {C}lustering},
     author = {Niu, Chuang and Shan, Hongming and Wang, Ge},
     journal = {TIP},
     year = {2022},
   }

2021

1. 

   Low-dimensional manifold constrained disentanglement network for metal artifact reduction

   Chuang Niu, Wenxiang Cong, Feng-Lei Fan, and 4 more authors

   IEEE Transactions on Radiation and Plasma Medical Sciences, Oct 2021

   Bib

   @article{niu2021low,
     title = {Low-dimensional manifold constrained disentanglement network for metal artifact reduction},
     author = {Niu, Chuang and Cong, Wenxiang and Fan, Feng-Lei and Shan, Hongming and Li, Mengzhou and Liang, Jimin and Wang, Ge},
     journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},
     year = {2021},
     publisher = {IEEE},
   }

2. 

   Multiple window learning for CT metal artifact reduction

   Chuang Niu, Mengzhou Li, and Ge Wang

   In Developments in X-Ray Tomography XIII, Oct 2021

   Bib

   @inproceedings{niu2021multiple,
     title = {Multiple window learning for CT metal artifact reduction},
     author = {Niu, Chuang and Li, Mengzhou and Wang, Ge},
     booktitle = {Developments in X-Ray Tomography XIII},
     volume = {11840},
     pages = {161},
     year = {2021},
     organization = {SPIE},
   }

3. 

   Noise entangled GAN for low-dose CT simulation

   Chuang Niu, Ge Wang, Pingkun Yan, and 8 more authors

   Fully3D, Oct 2021

   Bib

   @article{niu2021noise,
     title = {Noise entangled GAN for low-dose CT simulation},
     author = {Niu, Chuang and Wang, Ge and Yan, Pingkun and Hahn, Juergen and Lai, Youfang and Jia, Xun and Krishna, Arjun and Mueller, Klaus and Badal, Andreu and Myers, KyleJ and others},
     journal = {Fully3D},
     year = {2021},
   }

2020

1. 

   GATCluster: Self-supervised Gaussian-ATtention network for image Clustering

   Chuang Niu, Jun Zhang, Ge Wang, and 1 more author

   In European Conference on Computer Vision, Oct 2020

   Bib Code

   @inproceedings{niu2020gatcluster,
     title = {{GATC}luster: Self-supervised {G}aussian-{AT}tention network for image {C}lustering},
     author = {Niu, Chuang and Zhang, Jun and Wang, Ge and Liang, Jimin},
     booktitle = {European Conference on Computer Vision},
     pages = {735--751},
     year = {2020},
     organization = {Springer},
   }

2. 

   Noise2sim–similarity-based self-learning for image denoising

   Chuang Niu, and Ge Wang

   arXiv preprint arXiv:2011.03384 (Accepted by TMI), Oct 2020

   Bib Code

   @article{niu2020noise2sim,
     title = {Noise2sim--similarity-based self-learning for image denoising},
     author = {Niu, Chuang and Wang, Ge},
     journal = {arXiv preprint arXiv:2011.03384 (Accepted by TMI)},
     year = {2020},
   }

2019

1. 

   Joint Classification and Instance Segmentation of Auroral Images for Automatic Computation of Arc Width

   Chuang Niu, Yiping Tang, Shenghan Ren, and 3 more authors

   In AGU Fall Meeting Abstracts, Oct 2019

   Bib

   @inproceedings{niu2019joint,
     title = {Joint Classification and Instance Segmentation of Auroral Images for Automatic Computation of Arc Width},
     author = {Niu, Chuang and Tang, Yiping and Ren, Shenghan and Hu, Ze-Jun and Desheng, Han and Liang, Jimin},
     booktitle = {AGU Fall Meeting Abstracts},
     volume = {2019},
     pages = {NG31A--0854},
     year = {2019},
   }

2. 

   Instance segmentation of auroral images for automatic computation of arc width

   Chuang Niu, Qiuju Yang, Shenghan Ren, and 4 more authors

   IEEE Geoscience and Remote Sensing Letters, Oct 2019

   Bib Code

   @article{niu2019instance,
     title = {Instance segmentation of auroral images for automatic computation of arc width},
     author = {Niu, Chuang and Yang, Qiuju and Ren, Shenghan and Hu, Haihong and Han, Desheng and Hu, Ze-Jun and Liang, Jimin},
     journal = {IEEE Geoscience and Remote Sensing Letters},
     volume = {16},
     number = {9},
     pages = {1368--1372},
     year = {2019},
     publisher = {IEEE},
   }

2018

1. 

   Weakly-and Semi-Supervised Semantic Segmentation for Aurora Image Classification

   Chuang Niu, Shenghan Ren, Haihong Hu, and 3 more authors

   In AGU Fall Meeting 2018, Oct 2018

   Bib

   @inproceedings{niu2018weakly,
     title = {Weakly-and Semi-Supervised Semantic Segmentation for Aurora Image Classification},
     author = {Niu, Chuang and Ren, Shenghan and Hu, Haihong and Desheng, Han and Hu, Zejun and Liang, Jimin},
     booktitle = {AGU Fall Meeting 2018},
     year = {2018},
     organization = {AGU},
   }

2. 

   Weakly supervised semantic segmentation for joint key local structure localization and classification of aurora image

   Chuang Niu, Jun Zhang, Qian Wang, and 1 more author

   IEEE Transactions on Geoscience and Remote Sensing, Oct 2018

   Bib Code

   @article{niu2018weaklz,
     title = {Weakly supervised semantic segmentation for joint key local structure localization and classification of aurora image},
     author = {Niu, Chuang and Zhang, Jun and Wang, Qian and Liang, Jimin},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     volume = {56},
     number = {12},
     pages = {7133--7146},
     year = {2018},
     publisher = {IEEE},
   }