What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Magnetic resonance imaging
- Statistics
Bjoern H. Menze mostly deals with Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Random forest.
His Artificial intelligence research includes themes of Fluid-attenuated inversion recovery, Machine learning and Data mining.
His Segmentation study combines topics from a wide range of disciplines, such as Deep learning, Magnetic resonance imaging and Convolutional neural network.
His Pattern recognition study frequently draws connections between related disciplines such as Univariate.
His Random forest research is multidisciplinary, incorporating elements of Classifier, Voxel, Feature and Feature.
His Image segmentation research is multidisciplinary, relying on both Brain tumor and Medical imaging.
His most cited work include:
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
- The Multimodal Brain TumorImage Segmentation Benchmark (BRATS) (1126 citations)
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Computer vision are his primary areas of study.
In the subject of general Artificial intelligence, his work in Image segmentation, Random forest and Convolutional neural network is often linked to Context, thereby combining diverse domains of study.
He has researched Pattern recognition in several fields, including Artificial neural network, Magnetic resonance spectroscopic imaging, Magnetic resonance imaging and Voxel.
The study incorporates disciplines such as Lesion, Image, Feature and Medical imaging in addition to Segmentation.
His Medical imaging study frequently draws parallels with other fields, such as Brain tumor.
His study in Iterative reconstruction extends to Deep learning with its themes.
He most often published in these fields:
- Artificial intelligence (68.06%)
- Pattern recognition (46.27%)
- Segmentation (34.63%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.06%)
- Pattern recognition (46.27%)
- Segmentation (34.63%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Machine learning.
His Artificial intelligence study typically links adjacent topics like Computer vision.
In his study, Image retrieval and Computed tomography is inextricably linked to Reinforcement learning, which falls within the broad field of Computer vision.
His Pattern recognition research incorporates themes from Deconvolution, Gold standard, Imaging phantom, Scanner and Perfusion scanning.
His studies in Segmentation integrate themes in fields like Domain, Grey matter, Synthetic data and Medical imaging.
His Deep learning research is multidisciplinary, incorporating perspectives in Scalability, Overfitting, Scalar and Radiology, Iterative reconstruction.
Between 2019 and 2021, his most popular works were:
- Cellular and Molecular Probing of Intact Human Organs (49 citations)
- Machine learning analysis of whole mouse brain vasculature. (39 citations)
- Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection. (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Magnetic resonance imaging
His primary areas of investigation include Artificial intelligence, Deep learning, Segmentation, Pattern recognition and Convolutional neural network.
He combines subjects such as Gold standard, Machine learning and Computer vision with his study of Artificial intelligence.
His studies deal with areas such as Image quality, End-to-end principle, Analytics, Software engineering and Iterative reconstruction as well as Deep learning.
His research in Segmentation intersects with topics in Artificial neural network, Homotopy, Similarity measure and Function.
His biological study spans a wide range of topics, including GRASP, Scalar, Matching, Relaxation and Steady-state free precession imaging.
His Convolutional neural network research incorporates elements of Brain atlas, Neuroscience, Brainstem, Brain vasculature and Vascular function.
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