Profile Detail

Dooman Arefan, PhD

Research Instructor, Faculty member

Interest (in 3 to 10 words): Research

Medical / Graduate School B.Sc. in Electrical/Computer Engineering
M.Sc. in Medical Imaging Engineering (Radiation Medicine)
Ph.D. in Medical Imaging Engineering (Radiation Medicine)
Clinical / Post-doctoral Fellowship Postdoctoral Associate, Department of Radiology, University of Pittsburgh
Current Radiological Society of North America (2017 - Present)
& Programs
Nuclear Medicine
Digital Imaging
Clinical Interest Artificial Intelligence (AI), Machine learning (ML), and Deep learning in medical imaging
Research Interest Deep learning for predicting breast cancer risk using normal mammograms.

Machine leaning/Radiomics analysis in breast cancer to predict Oncotype DX gene test outcomes using breast MRI scans.

Radiomics/Radiogenomics analysis in predicting cell line invasion in breast tumor micro-environment (TME).

Automatic breast tumor segmentation in DCE-MRI.

Automatic breast density classification.

Deep learning to predict outcomes in severe traumatic brain injury patients using head CT/MRI scans.

Selected Publications   Arefan, Dooman, Ruimei Chai, Min Sun, Margarita L. Zuley, and Shandong Wu. "Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features", Medical physics, 2020. (In press).

Arefan, Dooman, Aly A. Mohamed, Wendie A. Berg, Margarita L. Zuley, Jules H. Sumkin, and Shandong Wu. "Deep learning modeling using normal mammograms for predicting breast cancer risk." Medical physics 47, no. 1 (2020): 110-118. (Editors Choice)

Giacomo Nebbia, Qian Zhang, Dooman Arefan, Xinxiang Zhao, Shandong Wu, Pre-operative micro vascular invasion prediction using multi-parametric liver MRI radiomics, Journal of Digital Imaging, in press, May 2020.

Chai R, Ma H, Xu M, Arefan D, Cui X, Liu Y, Zhang L, Wu S, Xu K. Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences. Journal of magnetic resonance imaging: JMRI. 2019 Mar.

Dooman Arefan, Bingjie Zheng, David Dabbs, Rohit Bhargava, Shandong Wu, Multi-space enabled deep learning of breast tumors improves prediction of distant recurrence, SPIE, Medical Imaging, Feb, 2019.

Zhang, Lei, Dooman Arefan, Yuan Guo, and Shandong Wu. "Fully automated tumor localization and segmentation in breast DCEMRI using deep learning and kinetic prior." In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, vol. 11318, p. 113180Z. International Society for Optics and Photonics, 2020.

Lei Zhang, Ruimei Chai, Dooman Arefan, Jules Sumkin, Shandong Wu, Deep-learning method for tumor segmentation in breast DCE-MRI, SPIE, Medical Imaging, Feb, 2019.

Pease MW (MD), Arefan D (PhD), Fields D, Billigen J, Sharpless J, Puccio A, Hockberger K, Roy S, Casillo S, Okonkwo D, Wu S. Deep Neural Network Analysis of CT scans to Predict Outcomes in a Prospective Database of Severe Traumatic Brain Injury Patients. In JOURNAL OF NEUROSURGERY 2020 Apr 1 (Vol. 132, No. 4).

Dooman Arefan, Alireza Talebpour, Nasrin Ahmadinejhad, Alireza Kamali Asl, Automatic breast density classification using neural network", Journal of Instrumentation, 2015.

Dooman Arefan, Alireza Talebpour, Nasrin Ahmadinejhad, Alireza Kamali Asl, Calculation of the contrast of the calcification in digital mammography system: Gate validation'", Journal of Cancer Research and Therapeutics, 2015.

Dooman Arefan, Alireza Talebpour, Nasrin Ahmadinejhad, Alireza Kamali Asl, UltraFast Image Reconstruction of Tomosynthesis Mammography Using GPU", Journal of Biomedical Physics and Engineering, 2015.

A.Talebpour, D. Arefan and H. Mohamadlou, Automated Abnormal Mass Detection in the Mammogram Images Using Chebyshev Moments"", Research Journal of Applied Sciences, Engineering and Technology Maxwell Scientific Organization, 2012.

Jamil Abdolmohammadi, Mohsen Shafiee, Fariborz Faeghi, Alireza Zali, Dooman Arefan, Rouzbeh Motiei Langeroudi, Zahra Farshidfar, Ali Kiani Nazarlou, Ali Tavakkoli, Mohammad Yarham, Determination the Intra-axial brain tumors cellularity through the analysis of T2 Relaxation time of brain tumor before surgery using MATLAB software", Electronic Physician, 2016.

Abdolmohammadi, J., Faeghi, F., Arefan, D., Zali, A., Haghighatkhah, H., Amiri, J. The Role of Single Voxel MR Spectroscopy, T2 Relaxation Time and Apparent Diffusion Coefficient in Determining the Cellularity of Brain Tumors by MATLAB Software Asian Pacific Journal of Cancer Prevention, 2018.

Honors and  
The RSNA Trainee Research Prize, Recipient of the Dr Tapan K. Chaudhuri Trainee Research Prize In memory of Tandra R. Chaudhuri, PhD, and Tamasa R. Mallik, BA, (2019).

Awarded a "Georg Forster Research Fellowship", by the Selection Committee, Germany (2017).
PubMed Publications   See a listing of publications on PubMed, a service of the National Library of medicine.