Publications

2023

1. 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 Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, 2023
2. 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

2022

1. 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

   Bib LINK 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},
   }

2. 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

   Bib 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},
   }

3. PhD Thesis

   Interpretable Domain-Aware Learning for Neuroimage Classification

   Shuo Zhou

   University of Sheffield, 2022

   Bib LINK

   @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/31044/1/PhD_thesis_ShuoZhou_170272834.pdf},
   }

4. 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 LINK

   @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},
   }

5. Signal Processing

   Direct ICA on data tensor via random matrix modeling

   Liyan Song, Shuo Zhou, and Haiping Lu

   Signal Processing, 2022

   Bib LINK

   @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

1. 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 LINK 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},
   }

2. 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 LINK

   @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},
   }

3. 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 LINK

   @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

1. 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 LINK 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

1. 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 LINK 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},
   }

2. 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 LINK

   @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},
   }