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Lambert Leong

Lambert Leong

PhD Student

Lambert was a PhD student in the Molecular Bioscience and Bio-Engineering (MBBE) department at the University of Hawaiʻi during his time with the Shepherd Research Lab. His prior degrees include a Masters in computer science (focused on high-performance computing and simulation) and a Bachelors in biology, with three years of bio-tech industry experience developing an artificial cornea for transplant. His work with SRL focused on breast imaging and the use of machine learning and artificial intelligence for cancer risk analysis and detection.

Lambert is now working on the mainland — see his LinkedIn (above) for his current position, and his personal site for his projects and writings.

Publications (17)

  • Association of body composition measures to muscle strength using DXA, D3Cr, and BIA in collegiate athletes
    Cataldi D, Bennett JP, Quon BK, Leong LT, Kelly TL, Binder AM, Evans JW, PRADO C, Heymsfield SB, Shepherd J
    Scientific Reports, 2025· doi:10.1038/s41598-025-87918-4
  • Evaluation of body shape as a human body composition assessment in isolated conditions and remote environments
    Wong MC, Bennett JP, Leong LT, Liu YE, Kelly NN, Cherry JA, Kloza K, Li B, Iuliano S, Sibonga JD, Sawyer A, Ayton J, Shepherd J
    npj Microgravity, 2024· doi:10.1038/s41526-024-00412-5
  • Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans
    Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
    Communications Medicine, 2024· doi:10.1038/s43856-024-00434-w
  • Deep Learning Furthers the Understanding of Local Distributions of Fat and Muscle on Body Shape and Health Using 3D Surface Scans
    Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
    SSRN Electronic Journal, 2023· doi:10.2139/ssrn.4436398
  • Association of Muscle Strength to Body Composition Measures using DXA, D 3 Cr, and BIA in Collegiate Athletes
    Cataldi D, Bennett JP, Quon BK, Leong LT, Kelly TL, Evans WJ, Prado CM, Heymsfield SB, Shepherd J
    medRxiv, 2023· doi:10.1101/2023.05.17.23288849
  • Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity
    Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
    American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.07.010
  • Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry
    Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Wong JM, Ebbeling CB, Ludwig DS, Irving BA, Scott MC, Stampley J, Davis B, Johannsen NM, Matthews R, Vincellette C, Garber AK, ...
    American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.02.006
  • Accuracy and Precision of 3D Optical Imaging for Body Composition and their Associations to Metabolic Markers by Age, BMI, and Ethnicity
    Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
    medRxiv, 2022· doi:10.1101/2022.11.02.22281819
  • Quantitative Imaging Principles Improves Medical Image Learning
    Leong LT, Wong M, Glaser Y, Wolfgruber T, Heymsfield SB, Peter S, Shepherd J
    arXiv (Cornell University), 2022· doi:10.48550/arxiv.2206.06663
  • Abstract P3-01-13: Comparing portable and clinical ultrasound systems using 3D printed breast phantom inserts
    Valdez D, Fukui J, Wolfgruber T, Leong LT, Maskarinec G, Shepherd J
    Cancer Research, 2022· doi:10.1158/1538-7445.sabcs21-p3-01-13
  • Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging
    Glaser Y, Shepherd J, Leong LT, Wolfgruber T, Lui L, Sadowski P, Cummings SR
    Communications Medicine, 2022· doi:10.1038/s43856-022-00166-9
  • Three‐dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults
    Bennett JP, Liu YE, Quon BK, Kelly NN, Leong LT, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
    Obesity, 2022· doi:10.1002/oby.23470
  • Dual-energy three compartment breast imaging (3CB) for novel compositional biomarkers to improve detection of malignant lesions
    Leong LT, Malkov S, Drukker K, Niell BL, Sadowski P, Wolfgruber T, Greenwood H, Joe BN, Kerlikowske K, Giger ML, Shepherd J
    Research Square, 2021· doi:10.21203/rs.3.rs-292446/v1
  • Creating Accurate Representations of DXA Scans from 3D Optical Body Surface Scans for Arbitrary Regional Body Composition Analysis
    Leong LT, Wong MC, Liu YE, Kelly NN, PIAZZA M, GARRY S, Heymsfield SB, Shepherd J
    , 2021· doi:10.15221/21.35
  • Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions
    Leong LT, Malkov S, Drukker K, Niell BL, Sadowski P, Wolfgruber T, Greenwood H, Joe BN, Kerlikowske K, Giger ML, Shepherd J
    Communications Medicine, 2021· doi:10.1038/s43856-021-00024-0
  • Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women
    Zhu X, Wolfgruber T, Leong LT, Jensen MR, Scott CG, Winham SJ, Sadowski P, Vachon CM, Kerlikowske K, Shepherd J
    Radiology, 2021· doi:10.1148/radiol.2021203758
  • Three compartment breast machine learning model for improving computer-aided detection
    Leong LT, Giger ML, Drukker K, Kerlikowske K, Joe BN, Greenwood H, Malkov S, Niell BL, Shepherd J
    , 2020· doi:10.1117/12.2560092

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