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 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.
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.
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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The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
Paul M. Thompson;Jason L. Stein;Sarah E. Medland;Derrek P. Hibar.
Brain Imaging and Behavior (2014)
Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments.
Mina Kim;Mina Kim;Joseph Gillen;Joseph Gillen;Bennett A. Landman;Jinyuan Zhou;Jinyuan Zhou.
Magnetic Resonance in Medicine (2009)
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello.
arXiv: Computer Vision and Pattern Recognition (2019)
Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.
Bennett A. Landman;Jonathan A.D. Farrell;Jonathan A.D. Farrell;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith.
The future of digital health with federated learning
Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari.
npj Digital Medicine (2020)
Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group
Neda Jahanshad;Peter V. Kochunov;Emma Sprooten;Emma Sprooten;René C. Mandl.
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
Jonathan A.D. Farrell;Bennett A. Landman;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith.
Journal of Magnetic Resonance Imaging (2007)
Multi-parametric neuroimaging reproducibility: a 3-T resource study.
Bennett A. Landman;Bennett A. Landman;Alan J. Huang;Alan J. Huang;Aliya Gifford;Deepti S. Vikram;Deepti S. Vikram.
Non-local statistical label fusion for multi-atlas segmentation
Andrew J. Asman;Bennett A. Landman.
Medical Image Analysis (2013)
Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data
Peter Kochunov;Neda Jahanshad;Daniel Marcus;Anderson Winkler.
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