D-Index & Metrics Best Publications

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 D-index 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. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 8,432 257 World Ranking 7877 National Ranking 3676

Research.com Recognitions

Awards & Achievements

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

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

Yongyi Yang spends much of his time researching Artificial intelligence, Computer vision, Support vector machine, Image processing and Iterative reconstruction. His Artificial intelligence study integrates concerns from other disciplines, such as Cancer, Machine learning and Maximum a posteriori estimation. His research integrates issues of Content-based image retrieval and Image retrieval in his study of Support vector machine.

His study in Image processing is interdisciplinary in nature, drawing from both Discrete cosine transform, Representation, Signal processing, Algorithm and Projections onto convex sets. His research investigates the connection with Iterative reconstruction and areas like Single-photon emission computed tomography which intersect with concerns in Perfusion and Image quality. His biological study spans a wide range of topics, including Contextual image classification and Mammography.

His most cited work include:

  • A support vector machine approach for detection of microcalcifications (474 citations)
  • Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances (399 citations)
  • Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images (376 citations)

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

Yongyi Yang focuses on Artificial intelligence, Computer vision, Iterative reconstruction, Pattern recognition and Single-photon emission computed tomography. His Artificial intelligence research is multidisciplinary, relying on both Smoothing and Mammography. Yongyi Yang interconnects Motion blur, Imaging phantom, Nuclear medicine, Algorithm and Noise reduction in the investigation of issues within Iterative reconstruction.

Within one scientific family, he focuses on topics pertaining to Feature under Pattern recognition, and may sometimes address concerns connected to Computer-aided diagnosis. His Single-photon emission computed tomography research includes elements of Medical imaging, Reconstruction procedure, Dynamic imaging, Perfusion and Spect imaging. The study incorporates disciplines such as Contextual image classification and Image retrieval in addition to Support vector machine.

He most often published in these fields:

  • Artificial intelligence (68.97%)
  • Computer vision (41.38%)
  • Iterative reconstruction (33.72%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (68.97%)
  • Pattern recognition (27.59%)
  • Iterative reconstruction (33.72%)

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

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Iterative reconstruction, Nuclear medicine and Single-photon emission computed tomography. He has included themes like Breast cancer and Computer vision in his Artificial intelligence study. His work on Motion estimation as part of general Computer vision study is frequently connected to Noise, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

His study on Pattern recognition also encompasses disciplines like

  • Feature and related Artificial neural network, Content-based image retrieval and Image retrieval,
  • Deep learning together with Receiver operating characteristic and Classifier,
  • Filter that connect with fields like Noise reduction. His Iterative reconstruction study combines topics from a wide range of disciplines, such as Intensity and Respiratory system. The study incorporates disciplines such as Myocardial perfusion imaging, Cardiac perfusion, Perfusion, Perfusion scanning and Dose reduction in addition to Nuclear medicine.

Between 2015 and 2021, his most popular works were:

  • A context-sensitive deep learning approach for microcalcification detection in mammograms (33 citations)
  • Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning (31 citations)
  • Investigation of dose reduction in cardiac perfusion SPECT via optimization and choice of the image reconstruction strategy (19 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Computer vision

His primary scientific interests are in Nuclear medicine, Iterative reconstruction, Artificial intelligence, Pattern recognition and Myocardial perfusion imaging. His Nuclear medicine research includes elements of Perfusion scanning, Intensity, Dose reduction and Respiratory system. His study looks at the intersection of Iterative reconstruction and topics like Perfusion with Selection operator, Lasso, Area under the roc curve and Support vector machine.

His Artificial intelligence research is multidisciplinary, incorporating perspectives in Microcalcification and Biopsy. His research in Deep learning intersects with topics in Classifier, False positive paradox, Detector and Receiver operating characteristic. His work in Radiology addresses issues such as Receiver operating characteristic analysis, which are connected to fields such as Mammography.

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

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

Jinshan Tang;R.M. Rangayyan;Jun Xu;I. El Naqa.
international conference of the ieee engineering in medicine and biology society (2009)

731 Citations

A support vector machine approach for detection of microcalcifications

I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos.
IEEE Transactions on Medical Imaging (2002)

711 Citations

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

Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Circuits and Systems for Video Technology (1993)

614 Citations

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

Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Image Processing (1995)

498 Citations

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

Henry Stark;Yongi Yang;Yongyi Yang.
(1998)

497 Citations

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

Liyang Wei;Yongyi Yang;R.M. Nishikawa;Yulei Jiang.
IEEE Transactions on Medical Imaging (2005)

399 Citations

Machine Learning in Medical Imaging

Miles Wernick;Yongyi Yang;Jovan Brankov;Grigori Yourganov.
IEEE Signal Processing Magazine (2010)

366 Citations

Digital watermarking robust to geometric distortions

Ping Dong;J.G. Brankov;N.P. Galatsanos;Yongyi Yang.
IEEE Transactions on Image Processing (2005)

363 Citations

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

I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa.
IEEE Transactions on Medical Imaging (2004)

359 Citations

Multiple-image radiography

Miles N Wernick;Oliver Wirjadi;Oliver Wirjadi;Dean Chapman;Zhong Zhong.
Physics in Medicine and Biology (2003)

279 Citations

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