World's Best Scientists 2026 revealed!

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Computer Science

D-Index
39
Citations
9554
World Ranking
9566
National Ranking
4052

Research.com Recognitions

  • 2021 - IEEE Fellow For contributions to medical image recovery and analysis

Overview

Yongyi Yang is affiliated with the Illinois Institute of Technology in the United States. Their research spans the fields of computer science and medicine, with a strong focus on radiology, nuclear medicine, and imaging. Their work also involves artificial intelligence, molecular biology, computer vision and pattern recognition, and plant science.

Yang's publication record includes contributions to various research topics, including medical imaging techniques and applications, advanced MRI techniques, cardiac imaging and diagnostics, advanced graph neural networks, advanced X-ray and CT imaging, photosynthetic processes and mechanisms, and plant molecular biology research.

They have authored papers in several well-known venues, with frequent publications in arXiv (Cornell University), the Journal of Nuclear Cardiology, Medical Physics, the 2022 IEEE International Conference on Image Processing (ICIP), and Neurocomputing.

Yang's recent papers include:

  • Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks, 2020, IEEE Transactions on Medical Imaging
  • Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging, 2020, Medical Physics
  • A Low-Cost Multi-Failure Resilient Replication Scheme for High-Data Availability in Cloud Storage, 2020, IEEE/ACM Transactions on Networking
  • Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults, 2021, Human Brain Mapping
  • Regulation of chlorophyll biosynthesis by light-dependent acetylation of NADPH:protochlorophyll oxidoreductase A in Arabidopsis, 2023, Plant Science

The scientist frequently collaborates with several coauthors including P. Hendrik Pretorius, Michael A. King, Miles N. Wernick, Biao Xiong, and Junchi Liu.

Yang was awarded the IEEE Fellow distinction in 2021 for contributions to medical image recovery and analysis.

Best Publications

  • Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

    Jinshan Tang;R.M. Rangayyan;Jun Xu;I. El Naqa

  • A support vector machine approach for detection of microcalcifications

    I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos

  • Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images

    Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos

  • Vector Space Projections : A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics

    Henry Stark;Yongi Yang;Yongyi Yang

  • Projection-based spatially adaptive reconstruction of block-transform compressed images

    Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos

  • Machine Learning in Medical Imaging

    Miles Wernick;Yongyi Yang;Jovan Brankov;Grigori Yourganov

  • A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;Yulei Jiang

  • Digital watermarking robust to geometric distortions

    Ping Dong;J.G. Brankov;N.P. Galatsanos;Yongyi Yang

  • A similarity learning approach to content-based image retrieval: application to digital mammography

    I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa

  • Multiple-image radiography

    Miles N Wernick;Oliver Wirjadi;Oliver Wirjadi;Dean Chapman;Zhong Zhong

  • Relevance vector machine for automatic detection of clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;M.N. Wernick

  • Removal of compression artifacts using projections onto convex sets and line process modeling

    Yongyi Yang;N.P. Galatsanos

  • Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields

    Yusuf Artan;Masoom A Haider;Deanna L Langer;Theodorus H van der Kwast

  • Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class

    Xin Liu;D.L. Langer;M.A. Haider;Y. Yang

  • Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI.

    Sedat Ozer;Deanna L. Langer;Xin Liu;Masoom A. Haider

  • Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

    Liyang Wei;Yongyi Yang;Robert M. Nishikawa

  • Tomographic image reconstruction based on a content-adaptive mesh model

    J.G. Brankov;Yongyi Yang;M.N. Wernick

  • A fast approach for accurate content-adaptive mesh generation

    Yongyi Yang;M.N. Wernick;J.G. Brankov

  • Projection-based blind deconvolution

    Yongyi Yang;Nikolas P. Galatsanos;Henry Stark

  • A physical model of multiple-image radiography

    Gocha Khelashvili;Jovan G Brankov;Dean Chapman;Mark A Anastasio

Frequent Co-Authors

Miles N. Wernick
Miles N. Wernick Illinois Institute of Technology
Nikolas P. Galatsanos
Nikolas P. Galatsanos University of Ioannina
Robert M. Nishikawa
Robert M. Nishikawa University of Pittsburgh
Mark A. Anastasio
Mark A. Anastasio University of Illinois at Urbana-Champaign
Aggelos K. Katsaggelos
Aggelos K. Katsaggelos Northwestern University
Stephen C. Strother
Stephen C. Strother University of Toronto
Konstantinos Arfanakis
Konstantinos Arfanakis Illinois Institute of Technology
Aristidis Likas
Aristidis Likas University of Ioannina
David A. Bennett
David A. Bennett Rush University Medical Center
Rangaraj M. Rangayyan
Rangaraj M. Rangayyan University of Calgary

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