A photo of Lili He.

Assistant Professor, UC Department of Pediatrics

513-636-5515

Biography & Affiliation

Biography

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:

  1. Improve image quality. Image quality directly influences diagnostic quality of the examination by characterizing tissue compared to another and, ultimately, highlighting morphological or functional abnormality. We have developed novel and practical imaging optimization techniques to achieve high image quality, especially by focusing on optimizing the trade-offs between image quality (signal-to-noise ratio, contrast-to-noise-ratio, spatial resolution) and scan time.
  2. Establish automate image processing pipeline. It has been a challenging task to bring data from multimodal MR images, together into a cohesive framework to enable cross-subject comparisons and multi-modal analysis. We have established neonatal anatomical and functional MRI automatic processing pipelines to minimize human efforts.
  3. Detect pathology. Medical image segmentation separates structures of interests from the background and from each other. It plays an important role in diseases’ pattern recognition for further diagnosis, monitoring and treatment. We have reported a sequential development of objective methods to quantitatively diagnose diffuse matter abnormalities and other injuries.
  4. Identify patients at risk of disease/adverse outcome and identify novel imaging biomarkers. We have been developing clinically relevant disease diagnosis and risk prediction AI models for neurodevelopmental deficits, attention deficit hyperactivity disorder and autism, which will allow more frequent disease monitoring to assess treatment response/disease progression, and potentially improve overall outcomes and lower healthcare costs. Ultimately, the developed AI models will enhance our abilities to diagnose disease in a quantitative, noninvasive, patient-friendly manner as well as to provide more patient-centric, precision medicine.

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 CCHMC.

Research Interests

Machine learning; deep learning; medical image processing and analysis

Academic Affiliation

Assistant Professor, UC Department of Pediatrics

Divisions

Imaging, Developmental Biology, Neonatology



Blog Posts

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

Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. Parikh, NA; He, L; Illapani, VS P; Altaye, M; Folger, AT; Yeates, KO. The Journal of Pediatrics. 2020; 220:56-63.

Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Parikh, NA; He, L; Li, H; Illapani, VS P; Klebanoff, MA. Pediatric Neurology. 2020; 106:56-62.

Neonatal Functional and Structural Connectivity Are Associated with Cerebral Palsy at Two Years of Age. Merhar, SL; Gozdas, E; Tkach, JA; Parikh, NA; Kline-Fath, BM; He, L; Yuan, W; Altaye, M; Leach, JL; Holland, SK. American Journal of Perinatology: neonatal and maternal-fetal medicine. 2020; 37:137-145.

Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Kline, JE; Illapani, VS P; He, L; Altaye, M; Parikh, NA. Scientific Reports. 2019; 9.

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. He, L; Li, H; Dudley, JA; Maloney, TC; Brady, SL; Somasundaram, E; Trout, AT; Dillman, JR. American Journal of Roentgenology. 2019; 213:592-601.

Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Li, H; Parikh, NA; Wang, J; Merhar, S; Chen, M; Parikh, M; Holland, S; He, L. Frontiers in Neuroscience. 2019; 13.

Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Parikh, MN; Li, H; He, L. Frontiers in Computational Neuroscience. 2019; 13.

Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?. Gozdas, E; Parikh, NA; Merhar, SL; Tkach, JA; He, L; Holland, SK. Brain Structure and Function. 2018; 223:3665-3680.

Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. He, L; Wang, J; Lu, Z; Kline-Fath, BM; Parikh, NA. Pediatric Radiology: roentgenology, nuclear medicine, ultrasonics, CT, MRI. 2018; 48:1139-1151.

A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Li, H; Parikh, NA; He, L. Frontiers in Neuroscience. 2018; 12.