A photo of Lili He.

Assistant Professor, UC Department of Pediatrics

513-636-5515

Biography & Affiliation

Research Interests

Machine learning; deep learning; medical image processing and analysis

Academic Affiliation

Assistant Professor, UC Department of Pediatrics

Departments

Imaging, Developmental Biology

Education

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.

Publications

Li H, Parikh NA, He L. A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci. 2018;12:491.

He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA. Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. NeuroImage: Clinical. 2018;18:290-297.

He L, Wang J, Lu Z-L, Kline-Fath BM, Parikh NA. Optimization of Magnetization-Prepared Rapid Gradient Echo (MP-RAGE) Sequence for Neonatal Brain MRI. Pediatric Radiology. 2018.

Wang J, He L, Zheng H, Lu Z-L. Improving structural brain images acquired with the 3D FLASH sequence. Magn Reson Imaging. 2017;38:224-232.

He L, Parikh NA. Brain Functional Network Connectivity Development in Very Preterm Infants: The First Six Months. Journal of Early Human Development. 2016;98:29-35.

He L, Parikh NA. Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants with Diffuse White Matter Abnormalities. Pediatric Neurology. 2015.

Kaur S, Powell S, He L, Pierson CR, Parikh NA. Reliability and Repeatability of Quantitative Tractography Methods for Mapping Structural White Matter Connectivity in Preterm and Term Infants at Term-Equivalent Age. PLos ONE. 2014;9(1):e85807.

He L, Parikh NA. Automated Detection of White Matter Signal Abnormality using T2 Relaxometry: Application to Brain Segmentation on Term MRI in Very Preterm Infants. NeuroImage. 2013;64:328-340.

He L, Parikh NA. Atlas-guided Quantification of White Matter Signal Abnormalities on Term-equivalent Age MRI in Very Preterm Infants: Findings Predict Language and Cognitive Development at Two Years of Age. PLoS ONE. 2013;8(12):e85475.

Parikh NA, He L, Bonfante-Mejia E, Hochhauser L, Evans P, Burson K, Kaur S. Automatically Quantified Diffuse Excessive High Signal Intensity on MRI Predicts Cognitive Development in Preterm Infants. Pediatr Neurol. 2013;49(6):424-30.