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
Scoring systems developed by machine learning: intelligent but simple to use?
Scoring systems developed by machine learning: intelligent but simple to use?
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
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.
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.
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
Scoring systems developed by machine learning: intelligent but simple to use?
Scoring systems developed by machine learning: intelligent but simple to use?
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
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.
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
Scoring systems developed by machine learning: intelligent but simple to use?
Scoring systems developed by machine learning: intelligent but simple to use?
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
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.
