World's Best Scientists 2026 revealed!

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Computer Science

D-Index
66
Citations
15044
World Ranking
2356
National Ranking
1174

Research.com Recognitions

  • 2019 - SPIE Fellow

Overview

Berkman Sahiner is affiliated with the United States Food and Drug Administration. Their research spans multiple disciplines primarily in Medicine and Computer Science, with a focus on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Health Informatics, Pulmonary and Respiratory Medicine, and Biomedical Engineering.

The scientist's work emphasizes topics such as Radiomics and Machine Learning in Medical Imaging, Artificial Intelligence in Healthcare and Education, AI in cancer detection, COVID-19 diagnosis using AI, Machine Learning in Healthcare, Medical Imaging Techniques and Applications, and Advanced X-ray and CT Imaging.

Among notable papers authored or co-authored by Berkman Sahiner are:

  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms, 2020, JAMA Network Open
  • Data drift in medical machine learning: implications and potential remedies, 2023, British Journal of Radiology
  • Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment, 2023, Journal of Medical Imaging
  • AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging, 2022, Medical Physics
  • AI in medical physics: guidelines for publication, 2021, Medical Physics

Frequent co-authors collaborating with Berkman Sahiner include:

  • Nicholas Petrick
  • Ravi K. Samala
  • H. Kenny
  • Alexej Gossmann
  • Karen Drukker

Publications have appeared often in venues such as:

  • arXiv (Cornell University)
  • Journal of Medical Imaging
  • Medical Physics
  • BJR|Artificial Intelligence
  • The Journal of Open Source Software

Berkman Sahiner's contributions include work in ensuring fairness and addressing biases in AI models for medical image analysis, investigating challenges such as data drift in medical machine learning, and recommending best practices for AI and machine learning in diagnostic imaging.

The scientist was awarded the SPIE Fellow distinction in 2019.

Best Publications

  • Deep learning in medical imaging and radiation therapy.

    Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang

  • 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

  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

    Thomas Schaffter;Diana S. M. Buist;Christoph I. Lee;Yaroslav Nikulin

  • A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis

    Yiheng Zhang;Heang Ping Chan;Berkman Sahiner;Jun Wei

  • 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

  • Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

    Ted W. Way;Lubomir M. Hadjiiski;Berkman Sahiner;Heang Ping Chan

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

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

  • Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network

    Heang Ping Chan;Shih Chung B. Lo;Berkman Sahiner;Kwok Leung Lam

  • 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

  • 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

Frequent Co-Authors

Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Lubomir M. Hadjiiski
Lubomir M. Hadjiiski University of Michigan–Ann Arbor
Nicholas Petrick
Nicholas Petrick US Food and Drug Administration
Jun Wei
Jun Wei Harbin Institute of Technology
Metin N. Gurcan
Metin N. Gurcan Wake Forest University
Kyle J. Myers
Kyle J. Myers Texas A&M University
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Anne L. Martel
Anne L. Martel University of Toronto
Paul L. Carson
Paul L. Carson University of Michigan–Ann Arbor
Gerald E. Marti
Gerald E. Marti National Institutes of Health

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