Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Anil A Bharath"'
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0276799 (2022)
Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant's hands. However, such requirements can narrow the broader ado
Externí odkaz:
https://doaj.org/article/c1fd463ce0f8420e954c0e24a23d4ae4
Autor:
Kavitha Vimalesvaran, Sameer Zaman, James P. Howard, Nikoo Aziminia, Marilena Giannoudi, Henry Procter, Marta Varela, Fatmatulzehra Uslu, Ben Ariff, Nick Linton, Eylem Levelt, Anil A. Bharath, Graham D. Cole
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss 1, Pp 100005- (2024)
Background: Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires
Externí odkaz:
https://doaj.org/article/e9fa345f152e4e25b40e39f1fb7dd9cb
Autor:
Sameer Zaman, Kavitha Vimalesvaran, Digby Chappell, Marta Varela, Nicholas S. Peters, Hunain Shiwani, Kristopher D. Knott, Rhodri H. Davies, James C. Moon, Anil A. Bharath, Nick WF Linton, Darrel P. Francis, Graham D. Cole, James P. Howard
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss 1, Pp 101040- (2024)
Background: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images
Externí odkaz:
https://doaj.org/article/8ad249f6ef7f44a8b9f40612520a252f
Publikováno v:
Frontiers in Signal Processing, Vol 3 (2024)
Externí odkaz:
https://doaj.org/article/34c9c5caf85f4b09b6aed8c77443168e
Autor:
Nathan C. K. Wong, Sepehr Meshkinfamfard, Valérian Turbé, Matthew Whitaker, Maya Moshe, Alessia Bardanzellu, Tianhong Dai, Eduardo Pignatelli, Wendy Barclay, Ara Darzi, Paul Elliott, Helen Ward, Reiko J. Tanaka, Graham S. Cooke, Rachel A. McKendry, Christina J. Atchison, Anil A. Bharath
Publikováno v:
Communications Medicine, Vol 2, Iss 1, Pp 1-10 (2022)
Wong et al. describe a machine learning approach for visual auditing of lateral flow tests for SARS-CoV-2 antibodies. Their automated analysis shows strong agreement with experts and consistently better performance than non-expert study participants
Externí odkaz:
https://doaj.org/article/07b76ff56a8546e2bdfba18d8ae2889a
Publikováno v:
IET Computer Vision, Vol 12, Iss 8, Pp 1105-1111 (2018)
The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifyi
Externí odkaz:
https://doaj.org/article/4e30c1cff9d5481980f8f56c2abc3811
Publikováno v:
Sensors, Vol 21, Iss 14, p 4701 (2021)
Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerg
Externí odkaz:
https://doaj.org/article/1428e859e826405e82085081f4c2112c
Autor:
Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Publikováno v:
Neurocomputing. 493:143-165
Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practical approach is to train agents in simulation, and then transfer them
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5fde61d3142aca4e59313bc025d8fad
https://doi.org/10.1016/j.neucom.2022.04.005
https://doi.org/10.1016/j.neucom.2022.04.005
Publikováno v:
Neurocomputing. 468:396-406
In many real-world problems, reward signals received by agents are delayed or sparse, which makes it challenging to train a reinforcement learning (RL) agent. An intrinsic reward signal can help an agent to explore such environments in the quest for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e1d40350124ddab7617ad5e80c2b79e
https://doi.org/10.1016/j.neucom.2021.10.040
https://doi.org/10.1016/j.neucom.2021.10.040
Autor:
'Konstantinos Ntagiantas, Dimitrios Panagopoulos, Wing M. Poon, Danya Agha-Jaffar, Nicholas S. Peters, Chris D. Cantwell, Anil A. Bharath, Rasheda A. Chowdhury\\'
Publikováno v:
Computing in Cardiology Conference (CinC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d320de55973348e096edfd1c49d9cd10
https://doi.org/10.22489/cinc.2022.227
https://doi.org/10.22489/cinc.2022.227