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Computer Science

D-Index
52
Citations
9532
World Ranking
5137
National Ranking
2364

Overview

Joseph Y. Lo is affiliated with Duke University in the United States and has contributed extensively to the fields of Medicine and Computer Science, with a significant focus on Radiology, Nuclear Medicine and Imaging, and Artificial Intelligence. Their research spans several specialized subfields including Pulmonary and Respiratory Medicine, Biomedical Engineering, and Computer Vision and Pattern Recognition.

The scientist's work covers a variety of topics within medical imaging and AI applications, such as:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Advanced X-ray and CT Imaging
  • COVID-19 diagnosis using AI
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment
  • Digital Radiography and Breast Imaging

Joseph Y. Lo has published research in several venues, with frequent contributions to:

  • arXiv (Cornell University)
  • Radiology
  • Medical Physics
  • Medical Image Analysis
  • Medical Imaging 2020: Computer-Aided Diagnosis

Some of their recent notable papers include:

  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms, 2020, JAMA Network Open
  • Virtual clinical trials in medical imaging: a review, 2020, Journal of Medical Imaging
  • A case-based interpretable deep learning model for classification of mass lesions in digital mammography, 2021, Nature Machine Intelligence
  • 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

They have collaborated frequently with several researchers, including:

  • Ehsan Samei
  • Fakrul Islam Tushar
  • Ehsan Abadi
  • Maciej A. Mazurowski
  • Lars J. Grimm

Best Publications

  • 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

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

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

  • Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.

    J A Baker;P J Kornguth;J Y Lo;M E Williford

  • A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning.

    David Good;Joseph Lo;W. Robert Lee;Q. Jackie Wu

  • Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion.

    Jay A Baker;Eric L Rosen;Joseph Y Lo;Edgardo I Gimenez

  • Breast tomosynthesis: state-of-the-art and review of the literature.

    Jay A Baker;Joseph Y Lo

  • Prediction of breast cancer malignancy using an artificial neural network

    Carey E. Floyd;Joseph Y. Lo;A. Joon Yun;Daniel C. Sullivan

  • Knowledge-based IMRT treatment planning for prostate cancer

    Vorakarn Chanyavanich;Shiva K. Das;William R. Lee;Joseph Y. Lo

  • Virtual clinical trials in medical imaging: a review

    Ehsan Abadi;William P. Segars;Benjamin M. W. Tsui;Paul E. Kinahan

  • A framework for optimising the radiographic technique in digital X-ray imaging

    Ehsan Samei;James T. Dobbins;Joseph Y. Lo;Martin P. Tornai

  • Self-organizing map for cluster analysis of a breast cancer database

    Mia K. Markey;Joseph Y. Lo;Georgia D. Tourassi;Carey E. Floyd

  • Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors.

    Jonathan L. Jesneck;Joseph Y. Lo;Jay A. Baker

  • Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

    Georgia D. Tourassi;Brian Harrawood;Swatee Singh;Joseph Y. Lo

  • Digital breast tomosynthesis using an amorphous selenium flat panel detector

    M. Bissonnette;M. Hansroul;E. Masson;S. Savard

  • Artificial neural network: improving the quality of breast biopsy recommendations.

    J A Baker;P J Kornguth;J Y Lo;C E Floyd

  • Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data

    Nooshin Kiarashi;Adam C. Nolte;Gregory M. Sturgeon;William P. Segars

  • Optimization of exposure parameters in full field digital mammography

    Mark B. Williams;Priya Raghunathan;Mitali J. More;J. Anthony Seibert

  • Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.

    J Y Lo;J A Baker;P J Kornguth;J D Iglehart

  • Efficient Fourier-Wavelet Super-Resolution

    M Dirk Robinson;C A Toth;J Y Lo;S Farsiu

Frequent Co-Authors

Ehsan Samei
Ehsan Samei Duke University
Maciej A. Mazurowski
Maciej A. Mazurowski Duke University
Georgia D. Tourassi
Georgia D. Tourassi Oak Ridge National Laboratory
Carlo C. Maley
Carlo C. Maley Arizona State University
Benjamin J. Wiley
Benjamin J. Wiley Duke University
Sina Farsiu
Sina Farsiu Duke University
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Sayan Mukherjee
Sayan Mukherjee Duke University
Lawrence Carin
Lawrence Carin Duke University
Paul E. Kinahan
Paul E. Kinahan University of Washington

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