D-Index & Metrics Best Publications

Hengyong Yu

University of Massachusetts Lowell
United States

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 41 Citations 7,928 255 World Ranking 5484 National Ranking 2686

Overview

What is he best known for?

The fields of study he is best known for:

Artificial intelligence
Statistics
Algorithm

Hengyong Yu focuses on Iterative reconstruction, Artificial intelligence, Algorithm, Computer vision and Interior reconstruction. The Iterative reconstruction study combines topics in areas such as Correctness, Mathematical optimization, Minification and Computed tomography. His Artificial intelligence research includes themes of Tomography and Pattern recognition.

His study in the fields of Compressed sensing under the domain of Algorithm overlaps with other disciplines such as Hilbert transform and Component. His Computer vision study combines topics in areas such as Iterative method and Spectral imaging. Hengyong Yu combines subjects such as Singular value decomposition and Inverse problem with his study of Interior reconstruction.

His most cited work include:

Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss (467 citations)
Compressed sensing based interior tomography. (412 citations)
Low-Dose X-ray CT Reconstruction via Dictionary Learning (385 citations)

What are the main themes of his work throughout his whole career to date?

Hengyong Yu mostly deals with Iterative reconstruction, Algorithm, Artificial intelligence, Computer vision and Tomography. The concepts of his Iterative reconstruction study are interwoven with issues in Image quality, Detector, Medical imaging, Image processing and Computed tomography. He focuses mostly in the field of Algorithm, narrowing it down to topics relating to Imaging phantom and, in certain cases, Simultaneous Algebraic Reconstruction Technique.

In his study, which falls under the umbrella issue of Artificial intelligence, Noise reduction is strongly linked to Pattern recognition. His study in Computer vision is interdisciplinary in nature, drawing from both Tomosynthesis and Spiral. His work carried out in the field of Tomography brings together such families of science as Region of interest, Medical physics and Piecewise.

He most often published in these fields:

Iterative reconstruction (45.25%)
Algorithm (44.49%)
Artificial intelligence (42.21%)

What were the highlights of his more recent work (between 2017-2021)?

Iterative reconstruction (45.25%)
Algorithm (44.49%)
Artificial intelligence (42.21%)

In recent papers he was focusing on the following fields of study:

Iterative reconstruction, Algorithm, Artificial intelligence, Computed tomography and Image quality are his primary areas of study. Hengyong Yu has included themes like Material decomposition, Detector, Image processing, Norm and Image gradient in his Iterative reconstruction study. Hengyong Yu interconnects Energy, Projection and Rank in the investigation of issues within Algorithm.

His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition. His work deals with themes such as Object, Imaging phantom, Tomography and Medical diagnosis, which intersect with Computed tomography. His Image quality research incorporates elements of Tensor, Feature and Compressed sensing.

Between 2017 and 2021, his most popular works were:

Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss (467 citations)
Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography (134 citations)
Low-dose spectral CT reconstruction using image gradient ℓ0-norm and tensor dictionary. (47 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

Qingsong Yang;Pingkun Yan;Yanbo Zhang;Hengyong Yu.
IEEE Transactions on Medical Imaging (2018)

862 Citations

Compressed sensing based interior tomography.

Hengyong Yu;Ge Wang.
Physics in Medicine and Biology (2009)

616 Citations

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Qiong Xu;Hengyong Yu;Xuanqin Mou;Lei Zhang.
IEEE Transactions on Medical Imaging (2012)

576 Citations

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

Qingsong Yang;Pingkun Yan;Yanbo Zhang;Hengyong Yu.
arXiv: Computer Vision and Pattern Recognition (2017)

409 Citations

An outlook on x-ray CT research and development.

Ge Wang;Hengyong Yu;Bruno De Man.
Medical Physics (2008)

372 Citations

A soft-threshold filtering approach for reconstruction from a limited number of projections

Hengyong Yu;Ge Wang;Ge Wang.
Physics in Medicine and Biology (2010)

230 Citations

A general local reconstruction approach based on a truncated Hilbert transform

Yangbo Ye;Hengyong Yu;Yuchuan Wei;Ge Wang.
International Journal of Biomedical Imaging (2007)

226 Citations

Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM).

Hao Gao;Hengyong Yu;Hengyong Yu;Stanley Osher;Ge Wang;Ge Wang.
Inverse Problems (2011)

225 Citations

Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography

Yanbo Zhang;Hengyong Yu.
IEEE Transactions on Medical Imaging (2018)

199 Citations

High-order total variation minimization for interior tomography

Jiansheng Yang;Hengyong Yu;Ming Jiang;Ming Jiang;Ge Wang.
Inverse Problems (2010)

192 Citations

If you think any of the details on this page are incorrect, let us know.