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Shandong Wu

Shandong Wu

Program: Cancer Epidemiology and Prevention


My research interfaces a broad range of interdisciplinary in computational science and medicine for translational and clinical applications. My main research areas include computational biomedical imaging analysis, big (health) data coupled with machine/deep learning, imaging-based clinical studies, radiomics/radiogenomics, and artificial intelligence in clinical informatics/workflows. Current research interests center on computational breast imaging and clinical studies for investigating quantitative imaging-derived biomarkers, models, and systems for breast cancer screening, risk assessment, diagnosis, prognosis, and treatment, towards improving individualized clinical decision-making and precision medicine. My research also covers other diseases/organs, such as pancreatic cancer, liver cancer, gastric cancer, traumatic brain injury, cardiac arrest, intestinalis, orthopedics, obesity, organoid, etc.

My lab received the prestigious "RSNA (Radiological Society of North America) Trainee Research Award" twice in 2017 and 2019, and the Natus Resident/Fellow Award for Traumatic Brain Injury by 2021 AANS (American Association of Neurological Surgeons). My lab's research is supported by NIH/NCI/NIBIB, NSF, RSNA, UPMC Enterprise, Pittsburgh Health Data Alliance, Pittsburgh Foundation, Stanley Marks Research Foundation, Amazon AWS, Nvidia, and many internal funding sources. I have published over 150 journal papers and conference papers/abstracts in both the computing and clinical fields, including in Nature Cancer, Nature Communications, Radiology, Clinical Cancer Research, Breast Cancer Research, Surgery, Resuscitation, IEEE Journal of Biomedical and Health Informatics, Pattern Recognition, AI in Medicine, IEEE Transactions on Cybernetics, CVPR, MICCAI, ICCV, IJCAI, ICRA, etc. My research has been featured in hundreds of scientific news reports and media outlets in the world. I founded and currently lead the Pittsburgh Center for AI Innovation in Medical Imaging (CAIIMI).

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