What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Magnetic resonance imaging
Artificial intelligence, Segmentation, Computer vision, Magnetic resonance imaging and Pattern recognition are his primary areas of study.
William M. Wells regularly links together related areas like Expectation–maximization algorithm in his Artificial intelligence studies.
His biological study spans a wide range of topics, including Algorithm, Probabilistic logic, Anatomy and Atlas.
His Computer vision study frequently links to related topics such as Mutual information.
His Mutual information research focuses on subjects like Maximization, which are linked to Object model.
He interconnects Visualization, Automated segmentation and Set in the investigation of issues within Magnetic resonance imaging.
His most cited work include:
- Alignment by Maximization of Mutual Information (2394 citations)
- Multi-modal volume registration by maximization of mutual information (1753 citations)
- Multi-modal volume registration by maximization of mutual information (1753 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image registration.
As part of his studies on Artificial intelligence, William M. Wells often connects relevant areas like Magnetic resonance imaging.
His Computer vision research includes elements of White matter and Computer graphics.
William M. Wells combines subjects such as Kullback–Leibler divergence, Feature, Expectation–maximization algorithm, Probabilistic logic and Signed distance function with his study of Pattern recognition.
His Segmentation research incorporates elements of Atlas, Ground truth and Medical imaging.
His Image registration study integrates concerns from other disciplines, such as Similarity measure, Algorithm, Posterior probability and Interpolation.
He most often published in these fields:
- Artificial intelligence (87.68%)
- Computer vision (51.72%)
- Pattern recognition (44.33%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (87.68%)
- Image registration (27.83%)
- Pattern recognition (44.33%)
In recent papers he was focusing on the following fields of study:
William M. Wells mainly investigates Artificial intelligence, Image registration, Pattern recognition, Computer vision and Algorithm.
His Artificial intelligence study frequently involves adjacent topics like Magnetic resonance imaging.
His work deals with themes such as Data mining, Classifier, Mutual information, Probabilistic logic and Monotonic function, which intersect with Image registration.
In his study, Information theory is strongly linked to Joint entropy, which falls under the umbrella field of Mutual information.
William M. Wells combines subjects such as Artificial neural network and Medical imaging with his study of Pattern recognition.
His Computer vision course of study focuses on White matter and Fiber.
Between 2017 and 2021, his most popular works were:
- Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching (27 citations)
- Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching (27 citations)
- Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Magnetic resonance imaging
William M. Wells spends much of his time researching Artificial intelligence, Image registration, Pattern recognition, Computer vision and Magnetic resonance imaging.
In the subject of general Artificial intelligence, his work in Segmentation and Deep learning is often linked to Field, thereby combining diverse domains of study.
He is involved in the study of Segmentation that focuses on Image segmentation in particular.
William M. Wells is interested in Mutual information, which is a branch of Pattern recognition.
William M. Wells interconnects Visualization, White matter and Ultrasound in the investigation of issues within Computer vision.
His work carried out in the field of Magnetic resonance imaging brings together such families of science as Sørensen–Dice coefficient, Positron emission tomography and Hyperspectral imaging.
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