World's Best Scientists 2026 revealed!
Maciej A. Mazurowski

Maciej A. Mazurowski

D-Index & Metrics

Computer Science

D-Index
38
Citations
9596
World Ranking
10004
National Ranking
4219

Maciej A. Mazurowski publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Maciej A. Mazurowski sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 173 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Maciej A. Mazurowski D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Maciej A. Mazurowski sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Maciej A. Mazurowski is affiliated with Duke University in the United States. Their research spans the intersection of medicine and computer science, focusing extensively on radiology, nuclear medicine, and imaging. With a publication record emphasizing artificial intelligence and biomedical engineering, their work contributes to advancing medical imaging and healthcare technologies.

The scientist has published significantly in the fields of medicine and computer science, with notable subfields including:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Health Informatics

Key research topics covered by their work include:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • COVID-19 diagnosis using AI
  • Medical Image Segmentation Techniques
  • Artificial Intelligence in Healthcare and Education
  • Advanced Neural Network Applications
  • Medical Imaging Techniques and Applications

Their recent published papers illustrate the scope and focus of their work:

  • Segment anything model for medical image analysis: An experimental study, 2023, Medical Image Analysis
  • Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists, 2021, Computers in Biology and Medicine
  • A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images, 2021, JAMA Network Open
  • Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes, 2020, Medical Image Analysis
  • MRI image harmonization using cycle-consistent generative adversarial network, 2020, Medical Imaging 2020: Computer-Aided Diagnosis

Frequent collaborators in their research include:

  • Hanxue Gu
  • Nicholas Konz
  • Haoyu Dong
  • Joseph Y. Lo
  • Jichen Yang

Maciej A. Mazurowski has contributed to a variety of publication venues, with multiple papers featured in:

  • arXiv (Cornell University)
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • Zenodo (CERN European Organization for Nuclear Research)
  • Medical Image Analysis
  • Radiology Artificial Intelligence

Best Publications

  • A systematic study of the class imbalance problem in convolutional neural networks

    Mateusz Buda;Atsuto Maki;Maciej A. Mazurowski

  • 2008 Special Issue: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

    Maciej A. Mazurowski;Piotr A. Habas;Jacek M. Zurada;Joseph Y. Lo

  • Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

    Maciej A. Mazurowski;Piotr A. Habas;Jacek M. Zurada;Joseph Y. Lo

  • Segment anything model for medical image analysis: An experimental study

    Unknown

  • Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

    Maciej A. Mazurowski;Mateusz Buda;Ashirbani Saha;Mustafa R. Bashir

  • Radiogenomics: what it is and why it is important.

    Maciej A. Mazurowski

  • Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

    Mateusz Buda;Ashirbani Saha;Maciej A. Mazurowski

  • A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

    Ashirbani Saha;Michael R. Harowicz;Lars J. Grimm;Connie E. Kim

  • Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

    Ehab A. AlBadawy;Ashirbani Saha;Maciej A. Mazurowski

  • Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

    Elizabeth Hope Cain;Ashirbani Saha;Michael R. Harowicz;Michael R. Harowicz;Jeffrey R. Marks

  • Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

    Mateusz Buda;Benjamin Wildman-Tobriner;Jenny K. Hoang;David Thayer

  • Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.

    Lars J. Grimm;Jing Zhang;Maciej A. Mazurowski

  • Deep learning for identifying radiogenomic associations in breast cancer

    Zhe Zhu;Ehab Albadawy;Ashirbani Saha;Jun Zhang

  • Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics

    Jun Zhang;Ashirbani Saha;Zhe Zhu;Maciej A. Mazurowski

  • Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data

    Maciej A. Mazurowski;Kal Clark;Nicholas M. Czarnek;Parisa Shamsesfandabadi

  • Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility.

    Benjamin Wildman-Tobriner;Mateusz Buda;Jenny K. Hoang;William D. Middleton

  • Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists

    Albert Swiecicki;Nianyi Li;Jonathan O'Donnell;Nicholas Said

  • A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

    Mateusz Buda;Ashirbani Saha;Ruth Walsh;Sujata Ghate

  • Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features

    Bibo Shi;Lars J. Grimm;Maciej A. Mazurowski;Jay A. Baker

  • Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce

    Maciej A. Mazurowski

  • Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

    Rachel Lea Draelos;David Dov;Maciej A. Mazurowski;Joseph Y. Lo

  • Effects of MRI scanner parameters on breast cancer radiomics

    Ashirbani Saha;Xiaozhi Yu;Dushyant Sahoo;Maciej A. Mazurowski

  • Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis

    Maciej A. Mazurowski;Joseph Y. Lo;Brian P. Harrawood;Georgia D. Tourassi

Frequent Co-Authors

Joseph Y. Lo
Joseph Y. Lo Duke University
Georgia D. Tourassi
Georgia D. Tourassi Oak Ridge National Laboratory
Jacek M. Zurada
Jacek M. Zurada University of Louisville
Jeffrey R. Marks
Jeffrey R. Marks Duke University
Carlo C. Maley
Carlo C. Maley Arizona State University
Geoffrey D. Rubin
Geoffrey D. Rubin University of Arizona
Ehsan Samei
Ehsan Samei Duke University
Rendon C. Nelson
Rendon C. Nelson Duke University
Elizabeth A. Krupinski
Elizabeth A. Krupinski Emory University
Leslie M. Collins
Leslie M. Collins Duke University

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