What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
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.
His most cited work include:
- Respiratory motion models: a review. (261 citations)
- Respiratory motion models: a review. (261 citations)
- Design and evaluation of a system for microscope-assisted guided interventions (MAGI) (194 citations)
What are the main themes of his work throughout his whole career to date?
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
- Artificial neural network together with Parametric statistics,
- Process most often made with reference to Range.
Andrew P. King has included themes like Dynamic contrast-enhanced MRI, Breathing and Organ Motion in his Motion study.
He most often published in these fields:
- Artificial intelligence (60.85%)
- Computer vision (41.04%)
- Deep learning (14.62%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.85%)
- Deep learning (14.62%)
- Segmentation (13.68%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. (43 citations)
- Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. (29 citations)
- Plasma p-tau181 accurately predicts Alzheimer's disease pathology at least 8 years prior to post-mortem and improves the clinical characterisation of cognitive decline. (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
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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