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David Haynor, M.D., Ph.D.

Professor


Neuroradiology

Biography

Dr. Haynor is a neuroradiologist with a longstanding interest in image processing and computer applications in radiology.

Awards and Honors

Recent Publications (via Semantic Scholar)



Osteoporosis identification among previously undiagnosed individuals with vertebral fractures
L. Gold, R. Cody, W. K. Tan, et al. - Published 2022 - OSTEOPOROSIS INTERNATIONAL

The BRAIN Initiative Cell Census Network Data Ecosystem: A User’s Guide
M. Hawrylycz, M. Martone, P. Hof, et al. - Published 2022 - BIORXIV

Finding atrophy patterns of grey matter through orthonormal non-negative factorization
Wenbo Zhang, Kwun Chuen Gary Chan, D. Shibata, et al. - Published 2021 - MEDICAL IMAGING

Radiogenomic modeling predicts survival-associated prognostic groups in glioblastoma
Nicholas Nuechterlein, B. Li, A. Feroze, et al. - Published 2021 - NEURO-ONCOLOGY ADVANCES

Learning Cortical Parcellations Using Graph Neural Networks
K. Eschenburg, T. Grabowski, D. Haynor - Published 2021 - FRONTIERS IN NEUROSCIENCE

Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality.
X. V. Nguyen, M. A. Oztek, Devi D Nelakurti, et al. - Published 2020 - TOPICS IN MAGNETIC RESONANCE IMAGING : TMRI

Making Magnets More Attractive utions to Patient Comfort in MRI Physics and Engineering Contrib
C. Brunnquell, M. Hoff, N. Balu, et al. - Published 2020

Making Magnets More Attractive: Physics and Engineering Contributions to Patient Comfort in MRI.
C. Brunnquell, M. Hoff, N. Balu, et al. - Published 2020 - TOPICS IN MAGNETIC RESONANCE IMAGING : TMRI

DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation
Qifei Dong, Gang Luo, D. Haynor, et al. - Published 2020 - JOURNAL OF DIGITAL IMAGING

NIMG-46. RADIOGENOMIC FEATURES PREDICT CLINICALLY RELEVANT GENOME-WIDE ALTERATION SIGNATURES IN GLIOBLASTOMA
Nicholas Nuechterlein, B. Li, J. Fink, et al. - Published 2020