Publications | Shuo Zhou

2024

MICCAI

Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection

Mohammod NI Suvon, Prasun C Tripathi, Wenrui Fan, and 7 more authors

In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024

@inproceedings{suvon2024multimodal,
  title = {Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection},
  author = {Suvon, Mohammod NI and Tripathi, Prasun C and Fan, Wenrui and Zhou, Shuo and Liu, Xianyuan and Alabed, Samer and Osmani, Venet and Swift, Andrew J and Chen, Chen and Lu, Haiping},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages = {296--306},
  year = {2024},
  doi = {https://doi.org/10.1007/978-3-031-72378-0_28},
  organization = {Springer}
}

2023

MICCAI

Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI

Prasun C Tripathi, Mohammod NI Suvon, Lawrence Schobs, and 4 more authors

In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023

DOI Bib HTML Code

@inproceedings{tripathi2023tensor,
  title = {Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI},
  author = {Tripathi, Prasun C and Suvon, Mohammod NI and Schobs, Lawrence and Zhou, Shuo and Alabed, Samer and Swift, Andrew J and Lu, Haiping},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  doi = {https://doi.org/10.1007/978-3-031-43990-2_20},
  pages = {206--215},
  year = {2023},
  organization = {Springer}
}

IEEE TVSVT

First-Person Video Domain Adaptation with Multi-Scene Cross-Site Datasets and Attention-Based Methods

Xianyuan Liu, Shuo Zhou, Tao Lei, and 3 more authors

IEEE Transactions on Circuits and Systems for Video Technology, 2023

DOI Bib HTML Code

@article{liu2023first,
  title = {First-Person Video Domain Adaptation with Multi-Scene Cross-Site Datasets and Attention-Based Methods},
  author = {Liu, Xianyuan and Zhou, Shuo and Lei, Tao and Jiang, Ping and Chen, Zhixiang and Lu, Haiping},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology},
  year = {2023},
  publisher = {IEEE},
  doi = {10.1109/TCSVT.2023.3281671}
}

2022

IEEE TMI

Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity

Mwiza Kunda, Shuo Zhou, Gaolang Gong, and 1 more author

IEEE Transactions on Medical Imaging, 2022

DOI Bib HTML Code

@article{kunda2022improving,
  title = {Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity},
  author = {Kunda, Mwiza and Zhou, Shuo and Gong, Gaolang and Lu, Haiping},
  journal = {IEEE Transactions on Medical Imaging},
  doi = {10.1109/TMI.2022.3203899},
  year = {2022},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/9874890},
}

CIKM

PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python

Haiping Lu, Xianyuan Liu, Shuo Zhou, and 6 more authors

In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022

DOI Bib HTML Code

@inproceedings{pykale-cikm2022,
  title = {{PyKale}: Knowledge-Aware Machine Learning from Multiple Sources in {Python}},
  author = {Lu, Haiping and Liu, Xianyuan and Zhou, Shuo and Turner, Robert and Bai, Peizhen and Koot, Raivo and Chasmai, Mustafa and Schobs, Lawrence and Xu, Hao},
  booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM)},
  doi = {10.1145/3511808.3557676},
  year = {2022},
}

PhD Thesis

Interpretable Domain-Aware Learning for Neuroimage Classification

Shuo Zhou

University of Sheffield, 2022

Bib HTML

@phdthesis{zhou2022interpretable,
  title = {Interpretable Domain-Aware Learning for Neuroimage Classification},
  author = {Zhou, Shuo},
  year = {2022},
  school = {University of Sheffield},
  url = {https://etheses.whiterose.ac.uk/31044/},
}

EHJDH

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Samer Alabed, Johanna Uthoff, Shuo Zhou, and 8 more authors

European Heart Journal-Digital Health, 2022

Bib HTML

@article{alabed2022machine,
  title = {Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension},
  author = {Alabed, Samer and Uthoff, Johanna and Zhou, Shuo and Garg, Pankaj and Dwivedi, Krit and Alandejani, Faisal and Gosling, Rebecca and Schobs, Lawrence and Brook, Martin and Shahin, Yousef and others},
  journal = {European Heart Journal-Digital Health},
  volume = {3},
  number = {2},
  pages = {265--275},
  year = {2022},
  publisher = {Oxford University Press},
}

Signal Processing

Direct ICA on data tensor via random matrix modeling

Liyan Song, Shuo Zhou, and Haiping Lu

Signal Processing, 2022

Bib HTML

@article{song2022direct,
  title = {Direct ICA on data tensor via random matrix modeling},
  author = {Song, Liyan and Zhou, Shuo and Lu, Haiping},
  journal = {Signal Processing},
  volume = {196},
  pages = {108508},
  year = {2022},
  publisher = {Elsevier},
}

2021

EHJCI

A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Andrew J Swift, Haiping Lu, Johanna Uthoff, and 8 more authors

European Heart Journal-Cardiovascular Imaging, 2021

Bib HTML Code

@article{swift2021machine,
  title = {A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis},
  author = {Swift, Andrew J and Lu, Haiping and Uthoff, Johanna and Garg, Pankaj and Cogliano, Marcella and Taylor, Jonathan and Metherall, Peter and Zhou, Shuo and Johns, Christopher S and Alabed, Samer and others},
  journal = {European Heart Journal-Cardiovascular Imaging},
  volume = {22},
  number = {2},
  pages = {236--245},
  year = {2021},
  publisher = {Oxford University Press},
}

ISBI

Confidence-Quantifying Landmark Localisation For Cardiac MRI

Lawrence Schobs, Shuo Zhou, Marcella Cogliano, and 2 more authors

In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021

Bib HTML

@inproceedings{schobs2021confidence,
  title = {Confidence-Quantifying Landmark Localisation For Cardiac MRI},
  author = {Schobs, Lawrence and Zhou, Shuo and Cogliano, Marcella and Swift, Andrew J and Lu, Haiping},
  booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages = {985--988},
  year = {2021},
  organization = {IEEE},
}

MIA

Neuropsychiatric disease classification using functional connectomics-results of the connectomics in neuroimaging transfer learning challenge

Markus D Schirmer, Archana Venkataraman, Islem Rekik, and 16 more authors

Medical Image Analysis, 2021

Bib HTML

@article{schirmer2021neuropsychiatric,
  title = {Neuropsychiatric disease classification using functional connectomics-results of the connectomics in neuroimaging transfer learning challenge},
  author = {Schirmer, Markus D and Venkataraman, Archana and Rekik, Islem and Kim, Minjeong and Mostofsky, Stewart H and Nebel, Mary Beth and Rosch, Keri and Seymour, Karen and Crocetti, Deana and Irzan, Hassna and Hütel, Michael and Ourselin, Sebastien and Marlow, Neil and Melbourne, Andrew and Levchenko, Egor and Zhou, Shuo and Kunda, Mwiza and Lu, Haiping and others},
  journal = {Medical Image Analysis},
  volume = {70},
  pages = {101972},
  year = {2021},
  publisher = {Elsevier},
}

2020

AAAI

Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments

Shuo Zhou, Wenwen Li, Christopher Cox, and 1 more author

In Proceedings of the AAAI Conference on Artificial Intelligence, 2020

Bib HTML Code

@inproceedings{zhou2020side,
  title = {Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments},
  author = {Zhou, Shuo and Li, Wenwen and Cox, Christopher and Lu, Haiping},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {34},
  number = {04},
  pages = {6957--6964},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6179},
}

2019

MLMI

Improving whole-brain neural decoding of fmri with domain adaptation

Shuo Zhou, Christopher R Cox, and Haiping Lu

In International Workshop on Machine Learning in Medical Imaging, 2019

Bib HTML Code

@inproceedings{zhou2019improving,
  title = {Improving whole-brain neural decoding of fmri with domain adaptation},
  author = {Zhou, Shuo and Cox, Christopher R and Lu, Haiping},
  booktitle = {International Workshop on Machine Learning in Medical Imaging},
  pages = {265--273},
  year = {2019},
  organization = {Springer},
}

MLMI

Sturm: Sparse tubal-regularized multilinear regression for fmri

Wenwen Li, Jian Lou, Shuo Zhou, and 1 more author

In International Workshop on Machine Learning in Medical Imaging, 2019

Bib HTML

@inproceedings{li2019sturm,
  title = {Sturm: Sparse tubal-regularized multilinear regression for fmri},
  author = {Li, Wenwen and Lou, Jian and Zhou, Shuo and Lu, Haiping},
  booktitle = {International Workshop on Machine Learning in Medical Imaging},
  pages = {256--264},
  year = {2019},
  organization = {Springer},
}