His primary areas of study are Artificial intelligence, Computer vision, Motion, Magnetic resonance imaging and Image registration. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Imaging phantom and Breathing. His Computer vision research includes elements of Range, Operating microscope, Process and Motion.
His work deals with themes such as Field, Tracking system and Organ Motion, which intersect with Range. Andrew P. King interconnects Image and Volume in the investigation of issues within Motion. His Magnetic resonance imaging research integrates issues from Image resolution and Motion compensation.
Andrew P. King mainly focuses on Artificial intelligence, Computer vision, Deep learning, Pattern recognition and Motion. His Artificial intelligence study frequently draws connections between adjacent fields such as Magnetic resonance imaging. His study explores the link between Computer vision and topics such as k-space that cross with problems in Identification.
His Deep learning research incorporates elements of Interpretability, Prior probability and Data set. His Pattern recognition study also includes
His scientific interests lie mostly in Artificial intelligence, Deep learning, Segmentation, Pattern recognition and Magnetic resonance imaging. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision. He has researched Computer vision in several fields, including Sampling and k-space.
His Deep learning study combines topics in areas such as Range, Interpretability and Data set. His work on Image segmentation as part of general Segmentation study is frequently connected to Fully automated, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Pattern recognition study integrates concerns from other disciplines, such as Left ventricular myocardium, Atlas, Dilated cardiomyopathy and Kernel.
His primary scientific interests are in Artificial intelligence, Deep learning, Segmentation, Disease and Convolutional neural network. His work deals with themes such as Identification, Pipeline and Pattern recognition, which intersect with Artificial intelligence. His study in Pattern recognition is interdisciplinary in nature, drawing from both Autoencoder and Magnetic resonance imaging.
In general Segmentation study, his work on Image segmentation often relates to the realm of Cardiac Volume, thereby connecting several areas of interest. His Disease research is multidisciplinary, incorporating elements of Receptor and Clinical trial, Bioinformatics. His Convolutional neural network research is multidisciplinary, relying on both Range, Interpretability, Computer vision and k-space.
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Respiratory motion models: a review.
Jamie McClelland;David J. Hawkes;Tobias Schaeffter;Tobias Schaeffter;Andrew P. King;Andrew P. King.
Medical Image Analysis (2013)
Design and evaluation of a system for microscope-assisted guided interventions (MAGI)
P.J. Edwards;A.P. King;C.R. Maurer;D.A. De Cunha.
IEEE Transactions on Medical Imaging (2000)
Semi-supervised learning for network-based cardiac MR image segmentation
Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki.
medical image computing and computer-assisted intervention (2017)
Assessment of beta-amyloid deposits in human brain: a study of the BrainNet Europe Consortium
Irina Alafuzoff;Dietmar R. Thal;Thomas Arzberger;Nenad Bogdanovic.
Acta Neuropathologica (2009)
Mutations in the vesicular trafficking protein annexin A11 are associated with amyotrophic lateral sclerosis.
Bradley N Smith;Simon D Topp;Claudia Fallini;Hideki Shibata.
Science Translational Medicine (2017)
Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator.
Andrew P. King;Christian Buerger;Christian Buerger;Charalampos Tsoumpas;Charalampos Tsoumpas;Paul K. Marsden;Paul K. Marsden.
Medical Image Analysis (2012)
Alignment of sparse freehand 3-D ultrasound with preoperative images of the liver using models of respiratory motion and deformation
J.M. Blackall;G.P. Penney;A.P. King;D.J. Hawkes.
IEEE Transactions on Medical Imaging (2005)
Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation.
Christian Buerger;Tobias Schaeffter;Andrew P. King.
Medical Image Analysis (2011)
Fast generation of 4D PET-MR data from real dynamic MR acquisitions
Charalampos Tsoumpas;Christian Buerger;Andrew King;P. Mollet.
Physics in Medicine and Biology (2011)
Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.
Bram Ruijsink;Bram Ruijsink;Esther Puyol-Antón;Ilkay Oksuz;Matthew Sinclair.
Jacc-cardiovascular Imaging (2020)
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