2021 - IEEE Fellow For contributions to medical image recovery and analysis
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.
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.
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
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.
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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)
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)
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)
Projection-based spatially adaptive reconstruction of block-transform compressed images
Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Image Processing (1995)
Vector Space Projections : A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Henry Stark;Yongi Yang;Yongyi Yang.
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)
Machine Learning in Medical Imaging
Miles Wernick;Yongyi Yang;Jovan Brankov;Grigori Yourganov.
IEEE Signal Processing Magazine (2010)
Digital watermarking robust to geometric distortions
Ping Dong;J.G. Brankov;N.P. Galatsanos;Yongyi Yang.
IEEE Transactions on Image Processing (2005)
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)
Miles N Wernick;Oliver Wirjadi;Oliver Wirjadi;Dean Chapman;Zhong Zhong.
Physics in Medicine and Biology (2003)
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