Summary
Anil Anthony Bharath is Professor of Biologically-Inspired Computation & Inference in the Department of Bioengineering at Imperial. He holds a degree in Electrical & Electronic Engineering from UCL, and a PhD from Imperial College. From January, 2024, he was founding Academic Director of Imperial Global: Singapore and, with Professor Liu Yang of Nanyang Technological University, leads the IN-CYPHER research programme into the security of data and devices used in delivering AI-driven personalised healthcare.
Anil has worked on convolution-based architectures for visual processing since the mid 1990s. His current research is focussed on the use of deep networks for inference, and his group has recently published reviews on deep reinforcement learning and generative adversarial networks, as well as original research in these two fields.
Anil has published in the fields of pattern recognition, machine learning and signal processing, and has extensive experience in computer vision. In 1998, he demonstrated the first application of steerable filters (convolution-based architectures, with feedback) to shape detection. Later contributions included the use of Bayesian marginalisation in very early-stage computer vision, focus-of-attention methods, and custom-designed wavelet transforms to analyse images in a scalable manner.
In 2002, Dr. Bharath initiated the Basic Technology Project "Reverse Engineering Human Visual Processes", which created a blueprint for a scalable subset of processes in the human visual system, particularly of visual area V1.
In 2008, Dr. Bharath spun out the company Cortexica Vision Systems, which applies simplified models of the behaviour of biological visual neurons to the technology of visual search. Cortexica was acquired by Zebra Technologies in 2019. See https:/www.imperial.ac.uk/bici-lab for more details, as well as a brief discussion on how these models are related to modern convolutional neural networks.
Anil’s research publications can be found at the tab above, or on Google Scholar
Selected Publications
Journal Articles
Vimalesvaran K, Zaman S, Howard J, et al., 2024, Aortic stenosis assessment from the 3-chamber cine: ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity, Journal of Cardiovascular Magnetic Resonance, Vol:26, ISSN:1097-6647
Zaman S, Vimalesvaran K, Howard JP, et al., 2023, Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI, Journal of Medical Artificial Intelligence, Vol:6, ISSN:2617-2496
Gionfrida L, Rusli W, Kedgley A, et al., 2022, A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition, Electronics, Vol:11, ISSN:2079-9292
Lino M, Fotiadis S, Bharath AA, et al., 2022, Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics, Physics of Fluids, Vol:34, ISSN:1070-6631
Wong N, Meshkinfamfard S, Turbé V, et al., 2022, Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies, Communications Medicine, Vol:2, ISSN:2730-664X
Bharath A, Uslu F, Varela Anjari M, et al., 2022, LA-Net: A multi-task deep network for the segmentation of the left atrium, IEEE Transactions on Medical Imaging, Vol:41, ISSN:0278-0062, Pages:456-464
Dai T, Du Y, Fang M, et al., 2022, Diversity-augmented intrinsic motivation for deep reinforcement learning, Neurocomputing, Vol:468, ISSN:0925-2312, Pages:396-406
Zaman S, Petri C, Vimalesvaran K, et al., 2022, Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports, Radiology: Artificial Intelligence, Vol:4, ISSN:2638-6100
Patel R, Thong EHE, Batta V, et al., 2021, Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning, Radiology-artificial Intelligence, Vol:3, ISSN:2638-6100
Dai T, Liu H, Bharath A, 2020, Episodic self-imitation learning with hindsight, Electronics (basel), Vol:9, ISSN:2079-9292
Creswell A, Bharath AA, 2019, Denoising adversarial autoencoders, Ieee Transactions on Neural Networks and Learning Systems, Vol:30, ISSN:2162-2388, Pages:968-984
Chapters
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View, Editor(s): Wang, Dou, Fletcher, Speidel, Li, SPRINGER INTERNATIONAL PUBLISHING AG, Pages:571-580, ISBN:978-3-031-16430-9
Conference
Fotiadis S, Lino M, Hu S, et al., 2023, Disentangled Generative Models for Robust Prediction of System Dynamics, Pages:10222-10248
On Y, Vimalesvaran K, Galazis C, et al., 2023, Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps, Pages:648-657, ISSN:0302-9743
Lino M, Cantwell C, Fotiadis S, et al., 2022, REMuS-GNN: A rotation-equivariant model for simulating continuum dynamics, Algebraic and Geometric Learning Workshops 2022, ML Research Press, Pages:226-236
Dai T, Liu H, Arulkumaran K, et al., 2021, Diversity-based trajectory and goal selection with hindsight experience replay, 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Springer, Pages:32-45
Balaram S, Arulkumaran K, Dai T, et al., 2019, A maximum entropy deep reinforcement learning neural tracker, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), SPRINGER INTERNATIONAL PUBLISHING AG, Pages:400-408, ISSN:0302-9743
Garasto S, Nicola W, Bharath A, et al., 2019, Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex, 2019 9th International IEEE/EMBS Conference on Neural Engineering(NER),, IEEE