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 CV

 
 
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Location

 

Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Summary

Wenjia is a lecturer jointly at Data Science Institute and Department of Medicine, Imperial College London. His research focuses on developing novel image computing and machine learning algorithms for medical image analysis and applying the algorithms to clinical research. Currently, he is actively involved in the UK Biobank Imaging Study and UK Digital Heart Project.

Previously, Wenjia worked as a research associate at Biomedical Image Analysis Group, Department of Computing, working with Prof. Daniel Rueckert. Prior to that, he did his D.Phil at Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, under the supervision of Prof. Sir Michael Brady.

Please visit Wenjia's personal website for more information.

Publications

Journals

Ruijsink B, Puyol-Antón E, Oksuz I, et al., 2019, Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function., Jacc Cardiovasc Imaging

Bhuva AN, Treibel TA, De Marvao A, et al., 2019, Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning., Eur Heart J Cardiovasc Imaging

Qureshi N, Kim S, Cantwell C, et al., 2019, Voltage during atrial fibrillation is superior to voltage during sinus rhythm in localizing areas of delayed enhancement on magnetic resonance imaging: An assessment of the posterior left atrium in patients with persistent atrial fibrillation, Heart Rhythm, ISSN:1547-5271

Cox DJ, Bai W, Price AN, et al., 2019, Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle, Pediatric Research, Vol:85, ISSN:0031-3998, Pages:807-815

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