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Computer Science

D-Index
39
Citations
6918
World Ranking
9718
National Ranking
4109

Overview

Uwe Kruger is affiliated with Rensselaer Polytechnic Institute in the United States. Their research primarily focuses on the field of Medicine, with notable contributions to Surgery, Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience, Psychiatry and Mental Health, as well as Control and Systems Engineering.

The scientist's work covers several main topics, including Surgical Simulation and Training, Wound Healing and Treatments, Autism Spectrum Disorder Research, Child Nutrition and Feeding Issues, Cardiac, Anesthesia and Surgical Outcomes, Optical Imaging and Spectroscopy Techniques, and Family and Disability Support Research.

Uwe Kruger has published extensively in a number of peer-reviewed venues. Among the most frequent are:

  • Scientific Reports
  • arXiv (Cornell University)
  • Research Square (Research Square)
  • Control Engineering Practice
  • Journal of Personalized Medicine

Their recent publications include:

  • Deep neural networks for the assessment of surgical skills: A systematic review, 2021, The Journal of Defense Modeling and Simulation Applications Methodology Technology
  • Optimized collusion prevention for online exams during social distancing, 2021, npj Science of Learning
  • Functional Brain Imaging Reliably Predicts Bimanual Motor Skill Performance in a Standardized Surgical Task, 2020, IEEE Transactions on Biomedical Engineering
  • Association of AI quantified COVID-19 chest CT and patient outcome, 2021, International Journal of Computer Assisted Radiology and Surgery
  • Real-time Burn Classification using Ultrasound Imaging, 2020, Scientific Reports

Throughout their career, Uwe Kruger has collaborated frequently with several coauthors, including Suvranu De, Pingkun Yan, Jack Norfleet, Xavier Intes, and Juergen Hahn.

Best Publications

  • Deep learning in medical image registration: a survey

    Grant Haskins;Uwe Kruger;Pingkun Yan

  • 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

    Hongming Shan;Yi Zhang;Qingsong Yang;Uwe Kruger

  • Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.

    Hongming Shan;Atul Padole;Fatemeh Homayounieh;Uwe Kruger

  • Process monitoring approach using fast moving window PCA

    Xun Wang;Uwe Kruger;George W. Irwin

  • 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

    Hongming Shan;Yi Zhang;Qingsong Yang;Uwe Kruger

  • Moving window kernel PCA for adaptive monitoring of nonlinear processes

    Xueqin Liu;Xueqin Liu;Uwe Kruger;Tim Littler;Lei Xie

  • Recursive partial least squares algorithms for monitoring complex industrial processes

    Xun Wang;Uwe Kruger;Barry Lennox

  • Cointegration Testing Method for Monitoring Nonstationary Processes

    Qian Chen;Uwe Kruger;Andrew Y. T. Leung

  • DETECTION OF INCIPIENT TOOTH DEFECT IN HELICAL GEARS USING MULTIVARIATE STATISTICS

    N. Baydar;Q. Chen;A. Ball;U. Kruger

  • Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation

    Daniel P. Howsmon;Uwe Kruger;Stepan Melnyk;S. Jill James

  • Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods

    Hongming Shan;Atul Padole;Fatemeh Homayounieh;Uwe Krüger

  • Statistical‐based monitoring of multivariate non‐Gaussian systems

    Xueqin Liu;Lei Xie;Uwe Kruger;Tim Littler

  • Detecting abnormal situations using the Kullback-Leibler divergence

    Jiusun Zeng;Uwe Kruger;Jaap Geluk;Xun Wang

  • Learning deep similarity metric for 3D MR-TRUS image registration.

    Grant Haskins;Jochen Kruecker;Uwe Kruger;Sheng Xu

  • Nonlinear PCA With the Local Approach for Diesel Engine Fault Detection and Diagnosis

    Xun Wang;U. Kruger;G.W. Irwin;G. McCullough

  • Improved principal component monitoring of large-scale processes

    Uwe Kruger;Yiqi Zhou;Yiqi Zhou;George W Irwin

  • Diagnosis of Process Faults in Chemical Systems Using a Local Partial Least Squares Approach

    Uwe Kruger;Grigorios Dimitriadis

  • Statistical monitoring of complex multivariate processes : with applications in industrial process control

    Uwe Krüger;Lei Xie

  • Learning Deep Similarity Metric for 3D MR-TRUS Registration

    Grant Haskins;Jochen Kruecker;Uwe Kruger;Sheng Xu

  • Improved principal component monitoring using the local approach

    Uwe Kruger;Sukhbinder Kumar;Tim Littler

Frequent Co-Authors

George W. Irwin
George W. Irwin Queen's University Belfast
Juergen Hahn
Juergen Hahn Rensselaer Polytechnic Institute
Suvranu De
Suvranu De Rensselaer Polytechnic Institute
Pingkun Yan
Pingkun Yan Rensselaer Polytechnic Institute
Ge Wang
Ge Wang Rensselaer Polytechnic Institute
Xavier Intes
Xavier Intes Rensselaer Polytechnic Institute
Richard E. Frye
Richard E. Frye Barrow Neurological Institute
Stepan Melnyk
Stepan Melnyk University of Arkansas for Medical Sciences
Andrew Ball
Andrew Ball University of Huddersfield
Hongye Su
Hongye Su Zhejiang University

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