Imperial College London

DrWenjiaBai

Faculty of MedicineDepartment of Brain Sciences

Lecturer in Artificial Intelligence in Medicine
 
 
 
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Contact

 

+44 (0)20 7594 8291w.bai Website

 
 
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Location

 

Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Summary

I am a lecturer jointly at Department of Computing and Department of Brain Sciences, Imperial College London. I am also affiliated with Biomedical Image Analysis Group and Data Science Institute.

My research is at the interface between machine learning and medical imaging. I am interested in medical image segmentation, registration, reconstruction, classification and prediction problems, as well as their translation to clinical research and healthcare. I feel fortunate to work with colleagues that span the spectrum from computing to medicine.

Previously, I completed my D.Phil in Engineering Science at University of Oxford and my M.Eng and B.Eng in Automation at Tsinghua University.

Please visit my personal website for more information.

Selected Publications

Journal Articles

Bai W, Suzuki H, Huang J, et al., 2020, A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine, Vol:26, ISSN:1078-8956, Pages:1654-1662

Meyer H, Dawes T, Serrani M, et al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol:584, ISSN:0028-0836, Pages:589-594

Chen C, Qin C, Qiu H, et al., 2020, Deep learning for cardiac image segmentation: A review, Frontiers in Cardiovascular Medicine, Vol:7, ISSN:2297-055X, Pages:1-33

Tarroni G, Oktay O, Bai W, et al., 2019, Learning-based quality control for cardiac MR images, IEEE Transactions on Medical Imaging, Vol:38, ISSN:0278-0062, Pages:1127-1138

Bai W, Sinclair M, Tarroni G, et al., 2018, Automated cardiovascular magnetic resonance image analysis with fully convolutional networks, Journal of Cardiovascular Magnetic Resonance, Vol:20, ISSN:1097-6647, Pages:1-12

Bai W, Shi W, de Marvao A, et al., 2015, A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion, Medical Image Analysis, Vol:26, ISSN:1361-8423, Pages:133-145

Bai W, Shi W, Ledig C, et al., 2015, Multi-atlas segmentation with augmented features for cardiac MR images, Medical Image Analysis, Vol:19, ISSN:1361-8415, Pages:98-109

Bai W, Shi W, O'Regan DP, et al., 2013, A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images, IEEE Transactions on Medical Imaging, Vol:32, ISSN:0278-0062, Pages:1302-1315

Conference

Wang S, Tarroni G, Qin C, et al., Deep generative model-based quality control for cardiac MRI segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Chen C, Qin C, Qiu H, et al., 2020, Realistic adversarial data augmentation for MR image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Bai W, Chen C, Tarroni G, et al., 2019, Self-supervised learning for cardiac MR image segmentation by anatomicalposition prediction, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Bai W, Suzuki H, Qin C, et al., 2018, Recurrent neural networks for aortic image sequence segmentation with sparse annotations, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), ISSN:0302-9743

Qin C, Bai W, Schlemper J, et al., 2018, Joint learning of motion estimation and segmentation for cardiac MR image sequences, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer Verlag, Pages:472-480

Bai W, Oktay O, Sinclair M, et al., 2017, Semi-supervised learning for network-based cardiac MR image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer Verlag, Pages:253-260, ISSN:0302-9743

Oktay O, Bai W, Lee M, et al., 2016, Multi-input cardiac image super-resolution using convolutional neural networks, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Pages:246-254, ISSN:0302-9743

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