Juhun Lee, PhD
- Assistant Professor, Department of Radiology, Imaging Research Laboratory
Dr. Juhun Lee received his PhD in Electrical and Computer Engineering at The University of Texas at Austin in 2014. Dr. Lee joined the Imaging Research Laboratory of the Department of Radiology in July of 2014 as a Postdoctoral Associate and was promoted to Research Instructor on February 1, 2017. His research focuses on breast imaging with an emphasis on using quantitative information extracted from breast computed tomography (CT) images, mammograms, and digital breast tomosynthesis to characterize breast lesions, estimate breast cancer risk, and optimize image reconstruction. Dr. Lee has published 14 journal papers in the medical imaging field and two of them were honored as Editor’s Choice in Medical Physics. He has also presented over 20 presentations at national and international conferences. He recently received an R37 award from the NIH/NCI for his breast cancer risk estimation research. The R37 award allows an early stage investigator to have an opportunity for extending the research support for additional two years.
- Lee J, Nishikawa RM. Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform. Journal of Medical Imaging. 2019 Dec; 6(4):044502.
- Lee J, Nishikawa RM, Reiser I, Boone JM. Neutrosophic segmentation of breast lesions for dedicated breast computed tomography. Journal of Medical Imaging. 2018 Mar;5(1):014505.
- Lee Juhun, Nishikawa Robert M. Automated mammographic breast density estimation using a fully convolutional network. Medical Physics. 2018 Feb 19;45(3):1178–1190.
- Lee J, Nishikawa RM, Reiser I, Boone JM. Optimal reconstruction and quantitative image features for Computer-Aided Diagnosis tools for breast CT. Med Phys. 2017 May 1;44(5):1846–1856.
- Lee J, Nishikawa RM, Reiser I, Boone JM, Lindfors KK. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Med Phys. 2015 Sep 1;42(9):5479–5489.
o Early detection of breast cancer using deep learning algorithms on mammograms
o Quantitative analysis of breast tumor types
o Developing computer-aided diagnostic tools for designated breast CT
o Developing personalized breast screening tools
o Developing segmentation algorithms for mammograms and breast CT
R37CA248207 LEE (PI) 4/1/20 – 3/31/25 NIH/NCI
Developing a personalized breast cancer screening tool using sequential mammograms