- Computational biomedical imaging analysis
- Big (health) data coupled with machine/deep learning
- Imaging-based clinical studies
- Artificial intelligence in clinical informatics/workflow
Shandong Wu, PhD, is an Associate Professor (with tenure) in Radiology (primary) and several other computational sciences at Pitt, and he is an Adjunct Professor in the Machine Learning Department at the Carnegie Mellon University (CMU). Dr. Wu leads the Intelligent Computing for Clinical Imaging (ICCI) lab (16 trainee members and >20 clinician collaborators) and serves as the Technical Director for AI Innovations in Radiology at Pitt/UPMC. He is the founding director of the Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging (CAIIMI, https://www.aimi.pitt.edu/), which includes more than 90 multidisciplinary members from Pitt, UPMC, and CMU, working on advancing AI research and clinical translation. Dr. Wu’s background is in Computer Science (Computer Vision) with additional clinical training in radiology research. Dr. Wu’s main research areas include computational biomedical imaging analysis, artificial intelligence in clinical/translational applications, big (health) data coupled with machine/deep learning, imaging-based clinical studies, and radiomics/radiogenomics/radioproteomics. Dr. Wu’s research has been growing from focusing on breast cancer imaging (screening, risk assessment, diagnosis, prognosis, and treatment) to cover many other diseases/organs as well, such as brain injury, gastric cancer, intestinalis, orthopedics, liver cancer and transplantation, pancreatic cancer, lung cancer, cardiac arrest, obesity, etc. Dr. Wu is an advocator of and passionate about developing trustworthy medical imaging AI for clinical/translational applications. Dr. Wu received the Pitt Innovator Award in 2019, and his lab received the prestigious “RSNA Trainee Research Award” twice in 2017 and 2019. Dr. Wu’s research is supported by NIH/NCI, RSNA, UPMC Enterprises, Pittsburgh Foundation, Pittsburgh Health Data Alliance, Stanly Marks Research Foundation, University of Pittsburgh Physician (UPP) Foundation, Amazon, and Nvidia. As a PI he has received more than 5 million dollars in research funds over the past 5 years. Dr. Wu has published > 100 papers/abstracts in both computing and clinical fields and he mentored more than 30 students. Dr. Wu is a regular reviewer for many grant agencies/study sections, renowned journals, and conferences.
1.Long Gao, Lei Zhang, Chang Liu, Shandong Wu, Handling Imbalanced Medical Image Data: A Deep-Learning-Based One-Class Classification Approach, Artificial Intelligence in Medicine, Volume 108, August 2020, 101935.
2.Jian Zheng, Haotian Sun, Shandong Wu, Ke Jiang, Yunsong Peng, Xiaodong Yang, Fan Zhang, Ming Li, 3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection, IEEE Journal of Biomedical and Health Informatics, May 2020. (in press)
3.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, 2020 Jun 3. doi: 10.1007/s10278-020-00353-x
4.Long Gao and Shandong Wu, Response Score of Deep Learning for Out-of-Distribution Sample Detection of Medical Images, Journal of Biomedical Informatics, 2020 Jul;107:103442. doi: 10.1016/j.jbi.2020.103442.
5.Yan Li, Zhenlu Yang, Tao Ai, Shandong Wu, Liming Xia, Association of Initial CT Findings with Mortality in Older Patients with Coronavirus Disease 2019 (COVID-19), European Radiology, 2020 Jun 10: 18.
6.Yunsong Peng, Shandong Wu, Qiang Du, Zhongyi Wu, Gang Yuan, Xiaodong Yang, Qian Chen, Jian Zheng, A radiomics method to classify microcalcification clusters automatically detected in digital breast tomosynthesis, Medical Physics, 2020 Aug;47(8):3435-3446.doi: 10.1002/mp.
7.Degan Hao, Lei Zhang, Jules Sumkin, Aly Mohamed, and Shandong Wu, Inaccurate labels in weakly-supervised deep learning: Automatic identification and correction and their impact on classification performance, IEEE Journal of Biomedical and Health Informatics, Volume: 24, Issue: 9, Sept. 2020.
8.Dooman Arefan, Aly A. Mohamed, Wendie A. Berg, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu, Deep learning modeling using normal mammograms for predicting breast cancer risk, Medical Physics, Jan. 2020, 47(1):110-118 (An Editors Choice article).
9.Lei Zhang, Aly A. Mohamed, Ruimei Chai, Yuan Guo, Bingjie Zheng, Shandong Wu, Automated deep learning method for whole-breast segmentation in diffusion-weighted breast magnetic resonance images, Journal of Magnetic Resonance Imaging, 2020 Feb;51(2):635-643. doi: 10.1002/jmri.26860.
10.Ruimei Chai, He Ma, Mingjie Xu, Dooman Arefan, Xiaoyu Cui, Yi Liu, Lina Zhang, Shandong Wu, Ke Xu, 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, 07 March 2019, https://doi.org/10.1002/jmri.26701
11.Fan Zhang, Shandong Wu, Cheng Zhang, Qian Chen, Xiaodong Yang, Ke Jiang,and Jian Zheng, Multi-domain features for reducing false positives in automated detection of clustered microcalcifications in digital breast tomosynthesis, Medical Physics 46(3):1300-1308, 2019 Mar
12.Yong Wang, Wei Shi, Shandong Wu, Robust UAV-based Tracking Using Hybrid Classifiers, Machine Vision and Applications, Feb. 2019, Vol. 30, Issue 1, pp 125137
13.Xiangsheng Li, Shandong Wu, Dechang Li, Tao Yu, Hongxian Zhu, Yunlong Song, Limin Meng, Hongxia Fan, Lizhi Xie, Intravoxel incoherent motion combined with dynamic contrast-enhanced perfusion MRI of early cervical carcinoma: correlations between multi-modal parameters and HIF-1 expression, Journal of Magnetic Resonance Imaging, 2019 Jan. doi: 10.1002/jmri.26604
14.Sarah S. Aboutalib, Aly A. Mohamed, Wendie A. Berg, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu, Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening, Clinical Cancer Research, 24(23):5902-5909. 2018 Dec 1.
15.Wenjuan Ma, Yumei Zhao, Yu Ji, Xinpeng Guo, Xiqi Jian, Peifang Liu, Shandong Wu, Breast Cancer Molecular Subtypes Prediction by Mammographic Radiomics Features, Academic Radiology, 2018 Mar 8. pii: S1076-6332(18) 30052-7.
16.Aly A. Mohamed, Wendie A. Berg, Hong Peng, Yahong Luo, Rachel C. Jankowitz, Shandong Wu, A deep learning method for classifying mammographic breast density categories, Medical Physics, 2018 Jan; 45(1):314-321.
17.Aly A. Mohamed, Yahong Luo, Hong Peng, Rachel C. Jankowitz, Shandong Wu, Understanding clinical mammographic breast density assessment: a deep learning perspective, Journal of Digital Imaging, Sep 2017. doi: 10.1007/s10278-017-0022-2.
18.Shandong Wu, Margarita Zuley, Wendie A. Berg, Brenda F Kurland, Rachel Jankowitz, Jules Sumkin, David Gur, DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence, Scientific Reports, 7(1):2115, May 2017.
19.Shandong Wu, Wendie A. Berg, Margarita L. Zuley, Brenda F. Kurland, Rachel C. Jankowitz, Robert Nishikawa, David Gur, and Jules H. Sumkin, Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer, Breast Cancer Research, May 2016, 18:76, 2016.
20.Justin C Brown, Despina Kontos, Mitchell Schnall, Shandong Wu, Kathryn H Schmitz. The Dose-Response Effects of Aerobic Exercise on Body Composition and Breast Tissue among Women at High Risk for Breast Cancer: A Randomized Trial. Cancer Prevention Research, 2016 Jul; 9(7): 581-8.
21.Shandong Wu, Susan P. Weinstein, Michael J DeLeo III, Emily F. Conant, Jinbo Chen, Susan M. Domchek, Despina Kontos, Quantitative assessment of Background Parenchymal Enhancement in breast MRI predicts response to Risk-Reducing Salpingo-Oophorectomy: Preliminary evaluation in a cohort of BRCA1/2 mutation carriers, Breast Cancer Research, 17(1):67-77, May 2015.
22.Yong Wang, Shiqiang Hu, and Shandong Wu. Visual tracking based on group sparsity learning, Machine Vision and Applications, volume 26, issue 1, pp 127-139, Jan. 2015.
23.Shandong Wu, Susan P. Weinstein, Emily F. Conant, and Despina Kontos. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method. Medical Physics 40(12):122301-12, 2013.
24.Shandong Wu, Susan P. Weinstein, Emily F. Conant, Mitchell D. Schnall, and Despina Kontos, Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images, Medical Physics, vol. 40, no. 4, pp. 042301-12, Apr. 2013.
25.Shandong Wu, Susan P. Weinstein, and Despina Kontos, Atlas-Based Probabilistic Fibroglandular Tissue Segmentation in Breast MRI,Medical Image Computing & Computer-Assisted Intervention (MICCAI), Part II, Lecture Notes in Computer Science (LNCS) 7511, pp. 437-445. H. Delingette, P. Golland, K. Mori (eds.). Springer-Verlag Berlin Heidelberg, Sep. 2012.
26.Shandong Wu, Omar Oreifej, Mubarak Shah, Action Recognition in Videos Acquired by a Moving Camera Using Motion Decomposition of Lagrangian Particle Trajectories, International Conference on Computer Vision (ICCV2011), Barcelona, Spain, 6-13 Nov. 2011.
27.Shandong Wu and Y.F. Li, Motion Trajectory Reproduction from Generalized Signature Description, Pattern Recognition, vol. 43, no. 1, pp. 204-221, Jan. 2010.
28.Shandong Wu, Brian E. Moore, and Mubarak Shah, Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, USA, June 13-18, 2010.
29.Shandong Wu and Y.F. Li, Flexible Signature Descriptions for Adaptive Motion Trajectory Representation, Perception and Recognition, Pattern Recognition, vol. 42, no. 1, pp. 194-214, Jan. 2009.
30.Shandong Wu and Y.F. Li, On Signature Invariants for Effective Motion Trajectory Recognition, The International Journal of Robotics Research, vol. 27,no. 8, pp. 895-917, Aug. 2008
Honors and Awards:
Pitt Innovator Award 2019.
ISMRM Merit Award (magna cum laude), abstract entitled “Effect of Risk-Reducing Salpingo-Oophorectomy on Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement in BRCA1/2 Mutation Carriers: A Quantitative Assessment,” presented in ISMRM 2013.