What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Internal medicine
His primary areas of study are Artificial intelligence, Diffusion MRI, Pattern recognition, Magnetic resonance imaging and Segmentation.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Brain mapping and Computer vision.
His Diffusion MRI course of study focuses on Orientation and Algorithm and Accuracy and precision.
He combines subjects such as Deep learning and Neuroscience with his study of Pattern recognition.
Within one scientific family, Bennett A. Landman focuses on topics pertaining to Spinal cord under Magnetic resonance imaging, and may sometimes address concerns connected to Central nervous system and Anatomy.
His Segmentation research is multidisciplinary, incorporating elements of Image registration and Convolutional neural network.
His most cited work include:
- The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (508 citations)
- Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments. (419 citations)
- Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T (282 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Segmentation, Diffusion MRI and Magnetic resonance imaging.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision.
His Pattern recognition research is multidisciplinary, relying on both Image processing, Voxel, Data mining and Neuroimaging.
His studies in Segmentation integrate themes in fields like Similarity, Medical imaging, Image registration, Ground truth and Computed tomography.
The Diffusion MRI study combines topics in areas such as Orientation, White matter and Algorithm.
Bennett A. Landman has included themes like Statistics and Corpus callosum in his Fractional anisotropy study.
He most often published in these fields:
- Artificial intelligence (55.73%)
- Pattern recognition (36.45%)
- Segmentation (25.76%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (55.73%)
- Pattern recognition (36.45%)
- Segmentation (25.76%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Diffusion MRI and Deep learning.
His research integrates issues of Machine learning and Magnetic resonance imaging in his study of Artificial intelligence.
His research in the fields of Convolutional neural network overlaps with other disciplines such as Distortion.
His Diffusion MRI study incorporates themes from White matter, Encoding, Ground truth and Diffusion.
His work in White matter tackles topics such as Audiology which are related to areas like Corpus callosum.
His Deep learning study combines topics from a wide range of disciplines, such as Pixel, Algorithm, Brain segmentation and Test set.
Between 2019 and 2021, his most popular works were:
- The future of digital health with federated learning (53 citations)
- Tractography Reproducibility Challenge With Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge (18 citations)
- Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Internal medicine
Bennett A. Landman mainly focuses on Artificial intelligence, Pattern recognition, Diffusion MRI, Deep learning and Temporal lobe.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in White matter and Magnetic resonance imaging.
He has researched Pattern recognition in several fields, including Recurrent neural network, Leverage, Pipeline, MNIST database and Noise reduction.
In general Diffusion MRI study, his work on Tractography and Fractional anisotropy often relates to the realm of Quality assurance, thereby connecting several areas of interest.
His Fractional anisotropy research is multidisciplinary, incorporating perspectives in Orientation and Connectomics.
His studies in Deep learning integrate themes in fields like Artificial neural network, Algorithm, Receiver operating characteristic and Confidence interval.
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