I am a computer scientist and expert in magnetic resonance imaging (MRI) with a long-standing commitment to develop and validate robust, clinically-effective computer-aided diagnosis systems. My research areas include machine learning, deep learning and medical imaging. I am committed to lending my expertise in neuroimaging and computer science to facilitate major breakthroughs in the medical field by optimizing imaging acquisition and aiding doctors in disease diagnosis, outcome prediction, image segmentation and interpretation as well as treatment decision-making and assessment.
I have been leading our team of artificial intelligence (AI) for computer aided diagnosis (AI-CAD) to develop algorithms to:
The current AI technique is rapidly moving from an experimental phase to an implementation phase in many fields. It is expected that medical AI will surpass human performance in specific applications within the coming years. Physicians and patients will likely benefit from the human-AI interaction. Since I have distinguished myself as a front-runner in medical imaging-based AI, I look forward to leading this endeavor at Cincinnati Children's Hospital Medical Center.
BS: Electrical Engineering, Tsinghua University, Beijing, China, 1998.
MS: Computer Science, University of Missouri, Columbia, MO, 2003.
PhD: Computer Science and Engineering, University of Connecticut, Storrs, CT, 2008.
Post-Doc: Massachusetts General Hospital, Harvard Medical School, Boston, MA, 2010.
Machine learning; deep learning; medical image processing and analysis
Imaging, Developmental Biology, Neonatology
A novel collaborative self-supervised learning method for radiomic data. Neuroimage. 2023; 277:120229.
Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Medical Image Analysis. 2023; 87:102828.
Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics. 2023; 13:1571.
PREDICTION OF FONTAN OUTCOMES USING T2-WEIGHTED MRI RADIOMIC FEATURES AND MACHINE LEARNING. Journal of the American College of Cardiology. 2023; 81:1618.
Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology. Neuroimage. 2022; 264:119727.
A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants. Neuroimage. 2022; 260:119484.
Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatric Radiology: roentgenology, nuclear medicine, ultrasonics, CT, MRI. 2022; 52:2139-2148.
A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatric Radiology: roentgenology, nuclear medicine, ultrasonics, CT, MRI. 2022; 52:2227-2240.