D-Index & Metrics Best Publications

D-Index & Metrics

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 37 Citations 5,794 144 World Ranking 5273 National Ranking 2583

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Image segmentation and Segmentation. His study involves Object detection, Deep learning, Pattern recognition, Feature vector and Unsupervised learning, a branch of Artificial intelligence. His research integrates issues of Ground truth and Image noise in his study of Feature vector.

The various areas that Pingkun Yan examines in his Pattern recognition study include Image resolution, Image and Iterative reconstruction. The concepts of his Image segmentation study are interwoven with issues in Kadir–Brady saliency detector and Seam carving. In his research, Convolution and Boundary is intimately related to Convolutional neural network, which falls under the overarching field of Segmentation.

His most cited work include:

  • Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss (467 citations)
  • Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging (370 citations)
  • Manifold Regularized Sparse NMF for Hyperspectral Unmixing (246 citations)

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

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Image segmentation. Pingkun Yan focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Lung cancer. His research on Computer vision frequently links to adjacent areas such as Ultrasound.

His Pattern recognition research is multidisciplinary, incorporating elements of Image and Feature. His work deals with themes such as Artificial neural network, Image noise, Medical imaging and Fluorescence-lifetime imaging microscopy, which intersect with Deep learning. In Image noise, Pingkun Yan works on issues like Feature vector, which are connected to Ground truth.

He most often published in these fields:

  • Artificial intelligence (73.85%)
  • Computer vision (39.45%)
  • Pattern recognition (35.32%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (73.85%)
  • Deep learning (22.48%)
  • Computer vision (39.45%)

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

His primary areas of investigation include Artificial intelligence, Deep learning, Computer vision, Medical imaging and Machine learning. Pingkun Yan applies his multidisciplinary studies on Artificial intelligence and Context in his research. His Deep learning research is multidisciplinary, incorporating perspectives in Segmentation, Pattern recognition, Artificial neural network, Lung cancer screening and Image.

Pingkun Yan usually deals with Pattern recognition and limits it to topics linked to Pyramid and Leverage. His study in the fields of Multimodality image fusion under the domain of Computer vision overlaps with other disciplines such as Image-Guided Biopsy. The study incorporates disciplines such as Functional Brain Imaging, Key, Robustness and Training set in addition to Machine learning.

Between 2019 and 2021, his most popular works were:

  • Deep learning in medical image registration: a survey (111 citations)
  • Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (52 citations)
  • Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction (16 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

Pingkun Yan focuses on Artificial intelligence, Deep learning, Medical imaging, Segmentation and Convolutional neural network. Pingkun Yan combines subjects such as Effective diffusion coefficient and Biopsy with his study of Artificial intelligence. In his work, Feature is strongly intertwined with Feature extraction, which is a subfield of Deep learning.

His work carried out in the field of Medical imaging brings together such families of science as Predictive value of tests, Medical physics and Intensive care unit, Intensive care medicine. His work on Image segmentation as part of general Segmentation study is frequently connected to Gallbladder and Esophagus, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Image segmentation research is under the purview of Pattern recognition.

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

Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging

Peter A. Pinto;Paul H. Chung;Ardeshir R. Rastinehad;Angelo A. Baccala.
The Journal of Urology (2011)

566 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.
IEEE Transactions on Medical Imaging (2018)

555 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)

289 Citations

Manifold Regularized Sparse NMF for Hyperspectral Unmixing

Xiaoqiang Lu;Hao Wu;Yuan Yuan;Pingkun Yan.
IEEE Transactions on Geoscience and Remote Sensing (2013)

264 Citations

Learning 4D action feature models for arbitrary view action recognition

Pingkun Yan;S.M. Khan;M. Shah.
computer vision and pattern recognition (2008)

222 Citations

Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

Pingkun Yan;Xiaobo Zhou;Xiaobo Zhou;M. Shah;S.T.C. Wong.
international conference of the ieee engineering in medicine and biology society (2008)

195 Citations

Saliency Detection by Multiple-Instance Learning

Qi Wang;Yuan Yuan;Pingkun Yan;Xuelong Li.
IEEE Transactions on Systems, Man, and Cybernetics (2013)

149 Citations

3D Model based Object Class Detection in An Arbitrary View

Pingkun Yan;S.M. Khan;M. Shah.
international conference on computer vision (2007)

136 Citations

Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation

Pingkun Yan;Sheng Xu;Baris Turkbey;Jochen Kruecker.
IEEE Transactions on Biomedical Engineering (2010)

128 Citations

Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

Xianbin Cao;Changxia Wu;Pingkun Yan;Xuelong Li.
international conference on image processing (2011)

126 Citations

Best Scientists Citing Pingkun Yan

Baris Turkbey

Baris Turkbey

National Institutes of Health

Publications: 90

Peter L. Choyke

Peter L. Choyke

National Institutes of Health

Publications: 71

Bradford J. Wood

Bradford J. Wood

National Institutes of Health

Publications: 57

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 46

Yuan Yuan

Yuan Yuan

Huawei Technologies (China)

Publications: 45

Ge Wang

Ge Wang

Rensselaer Polytechnic Institute

Publications: 39

Maria J. Merino

Maria J. Merino

National Institutes of Health

Publications: 30

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 29

Mark Emberton

Mark Emberton

University College London

Publications: 28

Xiaoqiang Lu

Xiaoqiang Lu

Chinese Academy of Sciences

Publications: 27

Samir S. Taneja

Samir S. Taneja

New York University

Publications: 27

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 27

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 25

Hashim U. Ahmed

Hashim U. Ahmed

Imperial College Healthcare NHS Trust

Publications: 23

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 23

Baowei Fei

Baowei Fei

The University of Texas at Dallas

Publications: 23

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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