Artificial intelligence, Iterative reconstruction, Computer vision, Algorithm and Imaging phantom are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Lymph node and Pattern recognition. Quanzheng Li works mostly in the field of Lymph node, limiting it down to topics relating to Medical diagnosis and, in certain cases, Breast cancer and Test set, as a part of the same area of interest.
His work carried out in the field of Computer vision brings together such families of science as Feature learning, Penalty method, Prior probability and Detector. His research in the fields of Residual neural network overlaps with other disciplines such as Ordinary differential equation, Stochastic control and Bridging. His Imaging phantom research integrates issues from Reconstruction algorithm, Positron emission tomography, Scanner and Biomedical engineering.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Iterative reconstruction, Algorithm and Computer vision. His study in Deep learning, Convolutional neural network, Image quality, Noise reduction and Medical imaging falls within the category of Artificial intelligence. His Pattern recognition research incorporates themes from Artificial neural network, Noise and Image resolution.
The study incorporates disciplines such as Imaging phantom, Iterative method, Positron emission tomography, Attenuation and Monte Carlo method in addition to Iterative reconstruction. In his study, Function is strongly linked to Poisson distribution, which falls under the umbrella field of Algorithm. His work in the fields of Image processing overlaps with other areas such as Maximum a posteriori estimation.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Noise. His study in Convolutional neural network, Image segmentation, Noise reduction, Iterative reconstruction and Embedding is carried out as part of his Artificial intelligence studies. His Iterative reconstruction research focuses on subjects like Medical imaging, which are linked to Discriminative model and Image registration.
His Pattern recognition research incorporates elements of Positron emission tomography, Image, Magnetic resonance imaging and Neuroimaging. His study in Magnetic resonance imaging is interdisciplinary in nature, drawing from both Imaging phantom and Attenuation. His Deep learning research is multidisciplinary, incorporating elements of Image quality, Functional brain and Cohort.
Quanzheng Li mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Correction for attenuation and Functional magnetic resonance imaging. The Artificial intelligence study combines topics in areas such as Disease progression, Temporal Cortices, Standardized uptake value, Gradient echo and Functional brain. His Pattern recognition research includes themes of Cross-validation, Iterative reconstruction and Medical imaging.
His research in Iterative reconstruction intersects with topics in Imaging phantom, Attenuation, Magnetic resonance imaging and Voxel. His work in Deep learning addresses issues such as Image quality, which are connected to fields such as Image resolution, Noise reduction and Supervised learning. The concepts of his Functional magnetic resonance imaging study are interwoven with issues in Resting state fMRI, Neuroimaging and Data set.
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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken.
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
James H. Thrall;Xiang Li;Quanzheng Li;Cinthia Cruz.
Journal of The American College of Radiology (2018)
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu;Aoxiao Zhong;Quanzheng Li;Quanzheng Li;Bin Dong.
international conference on machine learning (2018)
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
Peter Bandi;Oscar Geessink;Quirine Manson;Marcory Van Dijk.
IEEE Transactions on Medical Imaging (2019)
Optimization and performance evaluation of the microPET II scanner for in vivo small-animal imaging.
Yongfeng Yang;Yuan Chuan Tai;Stefan Siegel;Danny F. Newport.
Physics in Medicine and Biology (2004)
Treadmill exercise elevates striatal dopamine D2 receptor binding potential in patients with early Parkinson's disease
Beth E. Fisher;Quanzheng Li;Angelo Nacca;George J. Salem.
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Kuang Gong;Jiahui Guan;Kyungsang Kim;Xuezhu Zhang.
IEEE Transactions on Medical Imaging (2019)
Exercise Elevates Dopamine D2 Receptor in a Mouse Model of Parkinson’s Disease: In Vivo Imaging with [18F]Fallypride
Marta G. Vučković;Quanzheng Li;Beth Fisher;Angelo Nacca.
Movement Disorders (2010)
Deep Learning-Based Image Segmentation on Multimodal Medical Imaging
Zhe Guo;Xiang Li;Heng Huang;Ning Guo.
IEEE Transactions on Radiation and Plasma Medical Sciences (2019)
Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network
Dufan Wu;Kyungsang Kim;Georges El Fakhri;Quanzheng Li.
IEEE Transactions on Medical Imaging (2017)
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