Biography
Abhirup Banerjee is a Royal Society University Research Fellow, Full Member of Faculty, and PI in the Department of Engineering Science, University of Oxford. His research is primarily focused on Multimodal Reconstruction of Digital Anatomy for Real-Time Clinical Interventions. Dr Banerjee received the BSc (Hons) and Master degrees in Statistics from the University of Calcutta and the Indian Statistical Institute, respectively. He obtained the PhD degree in Computer Science in March 2017 from the Indian Statistical Institute, Kolkata for his dissertation on Rough-Probabilistic Models for Segmentation and Bias Field Correction in Brain MRI.
He joined the University of Oxford as Postdoctoral Researcher in the Division of Cardiovascular Medicine (CVM), Radcliffe Department of Medicine in August 2017. Dr Banerjee’s research interest spans Biomedical Engineering, Computer Science, and classical Statistics, focusing on a range of topics including Biomedical Image Analysis, Machine Learning, AI, Geometric Deep Learning, Image Processing, Statistical Pattern Recognition, etc. He has served as PI to a Data Study Group project in the Alan Turing Institute. He has received the Young Scientist Award from the Indian Science Congress Association in the year 2016-2017.
Awards and Honours
- The Young Scientist Award from the Indian Science Congress Association in the year 2016-2017 in the Section of Information and Communication Science & Technology (including Computer Sciences).
Most Recent Publications
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.
Research Interests
Dr Banerjee’s research interests are primarily focused on (but not limited to) Multimodal Reconstruction and Analysis of Digital Anatomy for Real-Time Clinical Interventions; in particular Segmentation, Landmark Detection, Tracking, Motion Modelling, Reconstruction, Registration, and Fusion of 2D/3D/3D+t Human Anatomical Structures (Cardiovascular, Cerebral, Biliary System, etc.) and Physiological information from Multi-Modality including X-ray, Angiography, MRI, CT, US, etc.
Grants
- Multimodal Reconstruction of Digital Heart for Cardiac Interventions in Real-Time: The Royal Society University Research Fellowship, June 2022 (Principal Investigator).
- Development of A System for Real Time Integration and Display of Quantitative Multi-modality Data During Cardiac Catheterisation: British Heart Foundation, March 2020 (Co-Applicant 1).
Current Projects
- Multimodal Reconstruction of Digital Heart for Cardiac Interventions in Real-Time.
- Building Personalised Heart and Torso Models from Clinical MRI Scans for Simulation of Cardiac Electromechanics.
- Rough-Probabilistic Modelling for Brain MRI Analysis, etc.
Research Groups
Most Recent Publications
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.
DPhil & Postdoc Opportunities
If you are Interested in embarking on a journey together on solving Real-life Biomedical problems with Clinical Impacts, please contact me via Email, LinkedIn, or Twitter.
Current Open Positions:
- Postdoctoral Research Assistant (Deadline: Noon on 2 March 2023)
- Research Studentship in Biomedical Image Analysis (Deadline: Noon on 1 March 2023)
Most Recent Publications
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.
Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.