What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Machine learning
His primary scientific interests are in Artificial intelligence, Magnetic resonance imaging, Computer vision, Pattern recognition and Segmentation.
His Artificial intelligence study incorporates themes from Brain tumor and Machine learning.
His biological study spans a wide range of topics, including Text mining, Solver, Order and Pathology.
His Pathology research includes elements of Grey matter and Abnormality.
His research integrates issues of Computer graphics, Computer graphics, Medical imaging and Pattern recognition in his study of Computer vision.
His Pattern recognition research integrates issues from Independence and Voxel.
His most cited work include:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (419 citations)
- Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. (392 citations)
What are the main themes of his work throughout his whole career to date?
Yong Fan mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Segmentation.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Functional connectivity.
The various areas that Yong Fan examines in his Pattern recognition study include Resting state fMRI, Image and Feature.
His Deep learning research also works with subjects such as
- Recurrent neural network which connect with Cognition,
- Disease that intertwine with fields like Hippocampal formation.
His research in Segmentation intersects with topics in Image processing, Magnetic resonance imaging and Image quality.
As part of one scientific family, Yong Fan deals mainly with the area of Magnetic resonance imaging, narrowing it down to issues related to the Neuroimaging, and often White matter.
He most often published in these fields:
- Artificial intelligence (60.94%)
- Pattern recognition (39.84%)
- Deep learning (17.58%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.94%)
- Deep learning (17.58%)
- Pattern recognition (39.84%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Deep learning, Pattern recognition, Neuroscience and Neuroimaging.
Yong Fan interconnects Machine learning, Receiver operating characteristic and Dementia in the investigation of issues within Artificial intelligence.
His work deals with themes such as Convolutional neural network, Artificial neural network, Ultrasound, Disease and Survival analysis, which intersect with Deep learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Regularization, Image and Ultrasound imaging.
Many of his research projects under Neuroscience are closely connected to Association with Association, tying the diverse disciplines of science together.
His study in Neuroimaging is interdisciplinary in nature, drawing from both White matter, Magnetic resonance imaging, Brain development, Functional magnetic resonance imaging and Clinical psychology.
Between 2018 and 2021, his most popular works were:
- Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. (50 citations)
- Individual Variation in Functional Topography of Association Networks in Youth (39 citations)
- MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Machine learning
His primary areas of investigation include Artificial intelligence, Deep learning, Neuroimaging, Cognition and Pattern recognition.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Radiology, Machine learning and Survival analysis.
His studies in Neuroimaging integrate themes in fields like White matter, Magnetic resonance imaging, Oncology, Internal medicine and Apolipoprotein E.
The concepts of his Magnetic resonance imaging study are interwoven with issues in Pathology, Dementia and Core needle.
The Cognition study combines topics in areas such as Recurrent neural network and Discriminative model.
His Pattern recognition research includes themes of Pixel, Image, Feature and Regression.
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