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Maciej A. Mazurowski

Maciej A. Mazurowski

D-Index & Metrics

Computer Science

D-Index
38
Citations
9596
World Ranking
10006
National Ranking
4219

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
Carlo C. Maley
Carlo C. Maley Arizona State University
Ehsan Samei
Ehsan Samei Duke University
Elizabeth A. Krupinski
Elizabeth A. Krupinski Emory University
Leslie M. Collins
Leslie M. Collins Duke University
Lawrence Carin
Lawrence Carin Duke University

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