World's Best Scientists 2026 revealed!

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Computer Science

D-Index
57
Citations
11402
World Ranking
3884
National Ranking
1837

Research.com Recognitions

  • 2017 - SPIE Fellow

Overview

Robert M. Nishikawa is affiliated with the University of Pittsburgh in the United States. Their research spans multiple disciplines within medicine and computer science, with a strong focus on artificial intelligence applications in medical imaging and oncology.

The scholar's work predominantly addresses topics related to:

  • AI in cancer detection
  • Radiomics and machine learning in medical imaging
  • Global cancer incidence and screening
  • Digital radiography and breast imaging
  • Artificial intelligence in healthcare and education
  • Breast cancer treatment studies
  • Generative adversarial networks and image synthesis

Main fields of study in which they have contributed include:

  • Medicine
  • Computer Science

Subfields of study in their publications feature:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Oncology
  • Pulmonary and Respiratory Medicine
  • Computer Vision and Pattern Recognition

Frequent publication venues where their work appears are:

  • Journal of Medical Imaging
  • Journal of Breast Imaging
  • Radiology
  • Journal of Clinical Oncology
  • Medical Imaging 2022: Computer-Aided Diagnosis

Their research includes studies on breast cancer detection, imaging technologies, and AI applications in clinical settings. Notable papers include:

  • Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis (2023), published in Radiology
  • Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification (2020), published in IEEE Access
  • Virtual Clinical Trials: Why and What (Special Section Guest Editorial) (2020), published in Journal of Medical Imaging
  • Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings (2022), published in Medical Physics
  • Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience (2023), published in Journal of Breast Imaging

Several frequent co-authors have collaborated with Robert M. Nishikawa, including:

  • Juhun Lee
  • Margarita L. Zuley
  • Andriy I. Bandos
  • Durwin Logue
  • Emily F. Conant

In 2017, Robert M. Nishikawa was recognized as a SPIE Fellow.

Best Publications

  • A support vector machine approach for detection of microcalcifications

    I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos

  • Improving breast cancer diagnosis with computer-aided diagnosis

    Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Charles E. Metz

  • A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;Yulei Jiang

  • A similarity learning approach to content-based image retrieval: application to digital mammography

    I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa

  • A receiver operating characteristic partial area index for highly sensitive diagnostic tests

    Y Jiang;C E Metz;R M Nishikawa

  • Malignant and benign clustered microcalcifications: automated feature analysis and classification.

    Y Jiang;R M Nishikawa;D E Wolverton;C E Metz

  • Computer-aided diagnosis in radiology: potential and pitfalls

    Kunio Doi;Heber MacMahon;Shigehiko Katsuragawa;Robert M Nishikawa

  • Current status and future directions of computer-aided diagnosis in mammography

    Robert M. Nishikawa

  • Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise.

    I. Reiser;R. M. Nishikawa

  • Methods for improving the accuracy in differential diagnosis on radiologic examinations

    Robert M. Nishikawa;Yulei Jiang;Kazuto Ashizawa;Kunio Doi

  • Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network

    Wei Zhang;Kunio Doi;Maryellen L. Giger;Yuzheng Wu

  • Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms.

    Emil Y. Sidky;Xiaochuan Pan;Ingrid S. Reiser;Robert M. Nishikawa

  • Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.

    Yuzheng Wu;Kunio Doi;Maryellen L. Giger;Robert M. Nishikawa

  • Effect of case selection on the performance of computer-aided detection schemes.

    Robert M. Nishikawa;Maryellen L. Giger;Kunio Doi;Charles E. Metz

  • Relevance vector machine for automatic detection of clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;M.N. Wernick

  • Potential of Computer-aided Diagnosis to Reduce Variability in Radiologists’ Interpretations of Mammograms Depicting Microcalcifications

    Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Alicia Y. Toledano

  • Scanned-projection digital mammography.

    Robert M. Nishikawa;Robert M. Nishikawa;Gordon E. Mawdsley;Gordon E. Mawdsley;Aaron Fenster;Aaron Fenster;Martin J. Yaffe;Martin J. Yaffe

  • Computer-aided method for image feature analysis and diagnosis in mammography

    Robert M. Nishikawa;Takehiro Ema;Hiroyuki Yoshida;Kunio Doi

  • Automated segmentation of digitized mammograms

    Ulrich Bick;Maryellen L. Giger;Robert A. Schmidt;Robert M. Nishikawa

  • Toward consensus on quantitative assessment of medical imaging systems.

    Charles E. Metz;Robert F. Wagner;Kunio Doi;David G. Brown

  • MALIGNANT AND BENIGN CLUSTERED MICROCALCIFICATIONS : AUTOMATED FEATURE ANALYSIS AND CLASSIFICATION. AUTHORS' REPLY

    G. A. P. De Kort;D. Beijerinck;J. J. M. Deurenberg;Y. Jiang

Frequent Co-Authors

Kunio Doi
Kunio Doi University of Chicago
Yongyi Yang
Yongyi Yang Illinois Institute of Technology
Charles E. Metz
Charles E. Metz University of Chicago
Miles N. Wernick
Miles N. Wernick Illinois Institute of Technology
Emil Y. Sidky
Emil Y. Sidky University of Chicago
Xiaochuan Pan
Xiaochuan Pan University of Chicago
Aaron Fenster
Aaron Fenster University of Western Ontario
Norbert J. Pelc
Norbert J. Pelc Stanford University
Ehsan Samei
Ehsan Samei Duke University
Webster K. Cavenee
Webster K. Cavenee University of California, San Diego

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