Grey Kuling, MA, PhD

Grey Kuling, PhD

Curriculum Fellow


Harvard Medical School

Pronouns: he/him/his

Dr. Grey Kuling is a Medical researcher with a focus on artificial intelligence in medical data analysis.  They hold a PhD in Medical Biophysics from the University of Toronto where they investigated the use of AI to quantify tissue features in breast MRI to assess cancer risk. Dr. Kuling is proficient in deep learning, natural language processing, and medical imaging analysis. They have authored multiple publications on breast MRI segmentation and AI applications in medical reports.  Dr. Kuling is a Curriculum Fellow at Harvard Medical School, affiliated with the Centre for Computational Biomedicine. Their current role focuses on teaching these valuable skills to HMS students, graduate students, researchers, and faculty.  Dr. Kuling’s research project tackles medical education with a focus on AI.  They are developing a software program that utilizes natural language processing and large language models in medical education. In addition to their research, Dr. Kuling has experience supervising students and teaching statistics courses. They are passionate about science communication and have presented their work at various conferences.

  • An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

    Authors: H Fashandi, G Kuling, Y Lu, H Wu, AL Martel

    Medical physics, 2019

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  • Intensity augmentation to improve generalizability of breast segmentation across different MRI scan protocols

    Authors: LS Hesse, G Kuling, M Veta, AL Martel

    IEEE Transactions on Biomedical Engineering, 2020

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  • Cardiac MRI segmentation with sparse annotations: ensembling deep learning uncertainty and shape priors

    Authors: F Guo, M Ng, G Kuling, G Wright

    Medical Image Analysis, 2022

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  • BI-RADS BERT and using section segmentation to understand radiology reports

    Authors: G Kuling, B Curpen, AL Martel

    Journal of Imaging, 2022

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  • Missed Breast Cancers on MRI in High-Risk Patients: A Retrospective Case–Control Study

    Authors: J Bilocq-Lacoste, R Ferre, G Kuling, AL Martel, PN Tyrrell, S Li, G Wang, .

    Tomography, 2022

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  • Domain adapted breast tissue segmentation in magnetic resonance imaging

    Authors: G Kuling, B Curpen, AL Martel

    15th International Workshop on Breast Imaging, 2020

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