World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
13636
World Ranking
5252
National Ranking
2420

Research.com Recognitions

  • 2019 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2018 - SPIE Fellow

Overview

Nicholas Petrick is affiliated with the US Food and Drug Administration in the United States. Their work primarily spans the fields of Medicine and Computer Science, focusing on specialized subfields such as Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Health Informatics, Pulmonary and Respiratory Medicine, and Biomedical Engineering.

Their research involves several main topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • AI in cancer detection
  • Advanced X-ray and CT Imaging
  • Lung Cancer Diagnosis and Treatment
  • Machine Learning in Healthcare
  • COVID-19 diagnosis using AI

The scientist has contributed to numerous publications in both journals and conference venues. Frequent publication venues where their work appears include:

  • arXiv (Cornell University)
  • Journal of Medical Imaging
  • Medical Physics
  • The Journal of Open Source Software
  • npj Digital Medicine

Recent papers authored or co-authored by Nicholas Petrick include:

  • "Data drift in medical machine learning: implications and potential remedies", 2023, British Journal of Radiology
  • "AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging", 2022, Medical Physics
  • "Transparency of artificial intelligence/machine learning-enabled medical devices", 2024, npj Digital Medicine
  • "SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment", 2021, Journal of Medical Imaging
  • "Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing", 2024, BJR|Artificial Intelligence

Frequent co-authors collaborating with Nicholas Petrick include:

  • Berkman Sahiner
  • H. Kenny
  • Ravi K. Samala
  • Mohammad Mehdi Farhangi
  • Gene Pennello

Nicholas Petrick has received several honors, including the Fellow of the Indian National Academy of Engineering (INAE) awarded in 2019 and the SPIE Fellow designation in 2018.

Best Publications

  • The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

    Samuel G. Armato;Geoffrey McLennan;Luc Bidaut;Michael F. McNitt-Gray

  • Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

    B. Sahiner;Heang-Ping Chan;N. Petrick;Datong Wei

  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

    Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan

  • Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

    David L Raunig;Lisa M McShane;Gene Pennello;Constantine Gatsonis

  • Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach

    Le Kang;Weijie Chen;Nicholas A. Petrick;Brandon D. Gallas

  • Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

    Heang-Ping Chan;Berkman Sahiner;Mark A. Helvie;Nicholas Petrick

  • An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

    N. Petrick;Heang-Ping Chan;B. Sahiner;Datong Wei

  • Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

    Heang-Ping Chan;Datong Wei;Mark A. Helvie;Berkman Sahiner

  • Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

    Heang Ping Chan;Berkman Sahiner;Kwok Leung Lam;Nicholas Petrick

  • Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

    Berkman Sahiner;Heang Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Computerized image analysis: estimation of breast density on mammograms.

    Chuan Zhou;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Improvement of mammographic mass characterization using spiculation measures and morphological features

    Berkman Sahiner;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization

    B. Sahiner;N. Petrick;Heang-Ping Chan;L.M. Hadjiiski

  • System and Method of Identifying a Potential Lung Nodule

    Heang-Ping Chan;Berkman Sahiner;Lubomir M. Hadjiyski;Chuan Zhou

  • Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue.

    Berkman Sahiner;Heang Ping Chan;Datong Wei;Nicholas Petrick

  • Noncalcified Lung Nodules: Volumetric Assessment with Thoracic CT

    Marios A. Gavrielides;Lisa M. Kinnard;Kyle J. Myers;Nicholas Petrick

  • Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.

    Heang-Ping Chan;Berkman Sahiner;Nicholas A. Petrick;Mark A. Helvie

  • Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

    Nicholas Petrick;Heang Ping Chan;Datong Wei;Berkman Sahiner

  • Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis

    Datona Wei;Heana Pina Chan;Mark A. Helvie;Berkman Sahiner

  • Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.

    Nicholas Petrick;Heang Ping Chan;Berkman Sahiner;Mark A. Helvie

Frequent Co-Authors

Berkman Sahiner
Berkman Sahiner United States Food and Drug Administration
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Lubomir M. Hadjiiski
Lubomir M. Hadjiiski University of Michigan–Ann Arbor
Kyle J. Myers
Kyle J. Myers Texas A&M University
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Jianhua Yao
Jianhua Yao Tencent (China)
Metin N. Gurcan
Metin N. Gurcan Wake Forest University
Neal H. Clinthorne
Neal H. Clinthorne University of Michigan–Ann Arbor
Alfred O. Hero
Alfred O. Hero University of Michigan–Ann Arbor
Jiang Li
Jiang Li Shanghai Jiao Tong University

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