Biography
Abhirup Banerjee is a Royal Society University Research Fellow and Principal Investigator 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
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
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.
The video below showcases some initial research imapcts of Dr Banerjee’s work for patients and the NHS.
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
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
DPhil & Postdoc Opportunities
If you are passionate about embarking on a journey together on solving Real-life Biomedical problems with Clinical Impacts, please contact me via Email, LinkedIn, or Twitter.
Most Recent Publications
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Editorial: Artificial intelligence applications for cancer diagnosis in radiology
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
