1. Lee J, Benveniste M, Odisio E, Court L, Lin S*, CT-based imaging metrics for identification of radiation-induced lung damage, 2023 (under review) medRxiv: doi: https://doi.org/10.1101/2022.12.18.22283636 

2. Lee J*, Zhao Q, Kent M, Platt S. Tumor Segmentation using temporal Independent Component Analysis for DCE-MRI, 2022 bioRxiv: doi: https://doi.org/10.1101/2022.12.16.520830 

3. Lee J, Ma J, Carter B, Court L. Lin S*, Effect of image registration on the estimation of pharmacokinetic parameters from DCE-MRI of patients with esophageal cancer, 2022 medRxiv: doi: https://doi.org/10.1101/2022.12.17.22283621

4. S. Turk, N. Wang, O. Kitis, J. Lee, S. Mohammed, T. Ma, R. Lobo, J. Kim, E. Liao, S. Camelo-Piragua, T. Johnson, L. Junck, A. Srinivasan, A. Rao, J. Bapuraj, Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomas, Radiology 2022 (Under Review)

5. N, Wang, Sevcan Turk, J. Lee, Shariq Mohammed, Tianwen Ma, Remy Lobo, John Kim, Eric Liao, Sandra Camelo- Piragua, Timothy Johnson, Larry Junck, Ashok Srinivasan, Arvind Rao, Jayapalli Bapuraj, Evaluating the Stability of Magnetic Resonance Imaging Features for the Prediction of Pseudoprogression. AJNR 2022 (under review)

1. Multimodal Machine Learning in Image-Based and Clinical biomedicine: Survey and Prospects. 

E. Warner, J. Lee, W. Hsu, T. Syeda-Mahmood, CE. Kahn, O. Gevaert, A. Rao.
International Journal of Coputer Vision, 2024; https://doi.org/10.1007/s11263-024-02032-8

2. Association of Graph-based Spatial features with overall survival status of Glioblastoma Patients

J. Lee, S. Narang, J. Martinez, G. Rao, A. Rao.
Scientific Reports, 2023;13:17046

3. Clustering-based Spatial Analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images

J. Lee, E. Warner, S. Shaikhouni, M. Bitzer, M. Kretzler, D. Gipson, S. Pennathur, K. Bellovich, Z. Bhat, C. Gadaegbeku, S. Massengill, K. Perumal, J. Saha, Y. Yang, J. Luo, X. Zhang, L. Mariani, J. B. Hodgin & A. Rao, for the C-PROBE Study.
Scientific Reports, 2023;13(1):12701

4. Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

E. Warner, J. Lee, S. Krishnan, N. Wang, S. Mohammed, A. Srinivasan, J. Bapuraj, A. Rao.
Proceedings of the IEEE Egn Med Biol Soc., 2023;1-4. PMID: 38083692

5. Federated Learning Enabling Big Data for Cancer Detection

S. Pati et.al., O. Yoshiaki, T. Moritani, S. Turk, ..., J. Lee, S. Prabhudesai, F. Moro´n, J. Mandel, K. Kamnitsas, B. Glocker, L. VM Dixon, M. Williams, P. Zampakis et. al. ()
Nature Communications, 2022;13:7346

6. Unsupervised machine learning for identifying important visual features through Bag-of-Words using histopathology data from Chronic Kidney Disease

J. Lee, E. Warner, S. Shaikhouni, M. Bitzer, M. Kretzler, D. Gipson, S. Pennathur, K. Bellovich, Z. Bhat, C. Gadaegbeku, S. Massengill, K. Perumal, J. Saha, Y. Yang, J. Luo, X. Zhang, L. Mariani, J. B. Hodgin & A. Rao, for the C-PROBE Study.
Scientific Reports, 2022;12:4832 

7. Head and Neck Radiotherapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: results from the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium 

Amanda Caissie MD PhD , Michelle Mierzwa MD , Clifton David Fuller MD PhD , Murali Rajaraman MD , Alex Lin MD , Andrew MacDonald MD , Richard Popple PhD , Ying Xiao PhD , Lisanne VanDijk MD , Peter Balter PhD , Helen Fong , Heping Xu PhD , Matthew Kovoor , Joonsang Lee PhD , Arvind Rao PhD , Mary Martel PhD , Reid Thompson MD , Brandon Merz PhD , John Yao PhD , Charles Mayo PhD
Advances in Radiation Oncology, 2022; doi: https://doi.org/10.1016/j.adro.2022.100925

8. Radiomics feature robustness as measured using an MRI phantom

J. Lee, A. Steinmann, Y. Ding, H. Lee, C. Owens, J. Wang, J. Yang, D. Followill, R. Ger, D. Mackin, L. Court.
Scientific Reports, 2021;11(1):3973,PMID: 33597610 

9. Stress Testing Pathology Models with Generated Artifacts

N. Wang, J. Kaplan, J. Lee, J. Hodgin, A. Udager, A. Rao.
Journal of Pathology Informatics, 2021;12:54, PMID: 35070483

10. Discriminating Pseudoprogression and True Progression in Diffuse Infiltrating Glioma using Multi-parametric MRI data through Deep Learning

J. Lee, N, Wang, S. Turk, S. Mohammed, R. Lobo, J. Kim, E. Liao, S. Camelo- Piragua, M. Kim, L. Junck, J. Bapuraj, A. Srinivasan, A. Rao
Scientific Reports, 2020;10(1):20331, PMID: 33230285

11. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

S. Milgrom, H. Elhalawani, J. Lee, Q. Wang, B. Dabaja, C. Pinnix, J. Gunther, L. Court, A. Rao, C. Fuller, M. Akhtari, M. Aristophanous, O. Mawlawi, H. Chuang, E. Sulman, H. Lee, F. Hagemeister, Y. Oki, M. Fanale, G. Smith
Scientific Reports, 2019; 4;9(1):1322

12. A Snapshot of Medical Physics Practice Patterns

K. Kisling, R. Ger, T. Netherton, C. Cardenas, C. Owens, B. Anderson, J. Lee, D. Rhee, S. Edward, S. Gay, Y. He, S. David, J. Yang, P. Nitsch, P. Balter, D. Urbauer, C. Peterson, L. Court, S. Dube
Journal of Applied Clinical Medical Physics 2018; 19(6)306-315

13. Cost-effective immobilization for whole brain radiation therapy

A. Rubinstein, S. Ingram, B. Anderson, S. Gay, X. Fave, R. Ger, R. McCarroll, C. Owens, T. Netherton, K. Kisling, L. Court, J. Yang, Y. Li, J. Lee, D. Mackin, and C. Cardenas
Journal of Applied Clinical Medical Physics. 2017;18(4):116-122

14. Radiomics in glioblastoma: current status, challenges and potential opportunities

S. Narang, M. Lehrer, D. Yang, J. Lee, A. Rao
Translational Cancer Research 2016;5(4):383-397

15. Computational resources for radiomics

L. Court, X. Fave, D. Mackin, J. Lee, J. Yang, L. Zhang
Translational Cancer Research 2016;5(4):340-348

16. DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancers

A. Saha, S. Banerjee, S. Kurtek, S. Narang, J. Lee, G. Rao, J. Martinez
NeuroImage: Clinical 2016;12:132-143

17. Texture Feature Ratios from Relative CBV Maps of Perfusion MRI are associated with Patient Survival in Glioblastoma 

J. Lee, R. Jain, K. Khalil, B. Griffith, R Bosca, G. Rao, A. Rao
American Journal of Neuroradiology, 2016; 37(1):37-43

18. Spatial habitat features derived from multiparametric magnetic resonance imaging data are associated with molecular subtype and 12-month survival in glioblastoma multiforme

J. Lee, S. Narang, J. Martinez, G. Rao, A. Rao
PLoS One, 2015;10(9), e0136557

19. Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation

J. Lee, S. Narang, J. Martinez, G. Rao, A. Rao
Journal of Medical Imaging, 2015;2(4), 0410016-041006 

20. Comparison of analytical and numerical analysis of the reference region model for DCE-MRI

J. Lee, J. Cardenas-Rodriguez, M.D. Pagel, S. Platt, M. Kent, Q. Zhao
Magnetic Resonance Imaging 2014;32(7):845-53.

21. An analysis of the pharmacokinetic parameter ratios in DCE-MRI using the reference region model

J. Lee, S. Platt, M. Kent, Q. Zhao
Magnetic Resonance Imaging 2012;30(1):26-35.

22. Differentiating intrinsic SERS spectra from a mixture by sampling induced composition gradient and independent component analysis

J. Lee*, J. Abell*, Q. Zhao, H. Szu, Y. Zhao
Analyst 2012;137(1):73-6. (*co-first authorship)

23. Quantification of DCE-MRI: pharmacokinetic parameter ratio between TOI and RR in reference region model

J. Lee,  Proceedings of the IEEE Egn Med Biol Soc, 2010:2837-40, PMID: 21095706