World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
18716
World Ranking
2313
National Ranking
1155

Research.com Recognitions

  • 2017 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2016 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2010 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to Computer Vision Pattern Recognition and Biomedical Engineering
  • 2007 - IEEE Fellow For contributions to computer vision and biomedical applications

Overview

Dmitry B. Goldgof is affiliated with the University of South Florida in the United States and has conducted extensive research in the domains of Medicine and Computer Science. Their work spans several subfields including Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pediatrics, Perinatology and Child Health, Pulmonary and Respiratory Medicine, and Computer Vision and Pattern Recognition.

The scientist's research covers significant topics such as Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, COVID-19 diagnosis using AI, Cell Image Analysis Techniques, Pediatric Pain Management Techniques, Infant Health and Development, and Lung Cancer Diagnosis and Treatment.

Frequent co-authors in their collaborative projects include Lawrence Hall, Peter R. Mouton, Saeed Alahmari, Ghada Zamzmi, and Md Sirajus Salekin.

Key publication venues where Dmitry B. Goldgof has contributed multiple works include:

  • arXiv (Cornell University)
  • IEEE Access
  • Tomography
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Computers in Biology and Medicine

Notable recent papers authored or co-authored by Dmitry B. Goldgof include:

  • Challenges for the Repeatability of Deep Learning Models, 2020, IEEE Access
  • Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets, 2020, Tomography
  • Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment, 2020, Computers in Biology and Medicine
  • Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions, 2023, ACM Computing Surveys
  • Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification, 2021, IEEE Access

Their contributions have been recognized through several fellowships and awards including:

  • Fellow of the Indian National Academy of Engineering (INAE), 2017
  • Fellow of the American Association for the Advancement of Science (AAAS), 2016
  • Fellow of the International Association for Pattern Recognition (IAPR), 2010, for contributions to Computer Vision Pattern Recognition and Biomedical Engineering
  • IEEE Fellow, 2007, for contributions to computer vision and biomedical applications

Best Publications

  • Radiomics: the process and the challenges

    Virendra Kumar;Yuhua Gu;Satrajit Basu;Anders Berglund

  • An experimental comparison of range image segmentation algorithms

    A. Hoover;G. Jean-Baptiste;X. Jiang;P.J. Flynn

  • Automatic tumor segmentation using knowledge-based techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;R. Velthuizen

  • Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

    R. Kasturi;D. Goldgof;P. Soundararajan;V. Manohar

  • Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

    M. Zhou;J. Scott;B. Chaudhury;L. Hall

  • Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms

    J. Candamo;M. Shreve;D.B. Goldgof;D.B. Sapper

  • Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

    Lynn M Fletcher-Heath;Lawrence O Hall;Dmitry B Goldgof;F.Reed Murtagh

  • Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

    Yoganand Balagurunathan;Yuhua Gu;Hua Wang;Virendra Kumar

  • Active Learning to Recognize Multiple Types of Plankton

    Tong Luo;Kurt Kramer;Dmitry B. Goldgof;Lawrence O. Hall

  • Deformable models in medical image analysis

    T. McInerney;D. Terzopoulos

  • MRI segmentation using fuzzy clustering techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;L.P. Clarke

  • Fast accurate fuzzy clustering through data reduction

    S. Eschrich;Jingwei Ke;L.O. Hall;D.B. Goldgof

  • Test–Retest Reproducibility Analysis of Lung CT Image Features

    Yoganand Balagurunathan;Virendra Kumar;Yuhua Gu;Jongphil Kim

  • Macro- and micro-expression spotting in long videos using spatio-temporal strain

    Matthew Shreve;Sridhar Godavarthy;Dmitry Goldgof;Sudeep Sarkar

  • Knowledge-based classification and tissue labeling of MR images of human brain

    Chunlin Li;D.B. Goldgof;L.O. Hall

  • Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging.

    A. A. Maudsley;A. Darkazanli;J. R. Alger;L. O. Hall

  • Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

    Lawrence O. Hall;Rahul Paul;Dmitry B. Goldgof;Gregory M. Goldgof

  • Fast fuzzy clustering

    Tai Wai Cheng;Dmitry B. Goldgof;Lawrence O. Hall

  • Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

    Rahul Paul;Samuel H Hawkins;Yoganand Balagurunathan;Matthew B Schabath

  • Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images

    S. Kumar;D. Goldgof

Frequent Co-Authors

Lawrence O. Hall
Lawrence O. Hall University of South Florida
Sudeep Sarkar
Sudeep Sarkar University of South Florida
Kevin W. Bowyer
Kevin W. Bowyer University of Notre Dame
Rangachar Kasturi
Rangachar Kasturi University of South Florida
Chandra Kambhamettu
Chandra Kambhamettu University of Delaware
Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Frank E. Muller-Karger
Frank E. Muller-Karger University of South Florida
Horst Bunke
Horst Bunke University of Bern
Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
Xiaoyi Jiang
Xiaoyi Jiang University of Münster

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