A photo of Lili He.

Lili He, PhD

  • Associate Professor, UC Department of Radiology



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 Cincinnati Children's Hospital Medical Center.


Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. He, L; Li, H; Chen, M; Wang, J; Altaye, M; Dillman, JR; Parikh, NA. Frontiers in Neuroscience. 2021; 15.

Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants. Chandwani, R; Kline, JE; Harpster, K; Tkach, J; Parikh, NA; Altaye, M; Arnsperger, A; Beiersdorfer, T; Bridgewater, K; Cahill, T; et al. Human Brain Mapping. 2021; 42:4708-4721.

Effects of prenatal opioid exposure on functional networks in infancy. Merhar, SL; Jiang, W; Parikh, NA; Yin, W; Zhou, Z; Tkach, JA; Wang, L; Kline-Fath, BM; He, L; Braimah, A; et al. Developmental Cognitive Neuroscience. 2021; 51.

Prenatal opioid exposure is associated with smaller brain volumes in multiple regions. Merhar, SL; Kline, JE; Braimah, A; Kline-Fath, BM; Tkach, JA; Altaye, M; He, L; Parikh, NA. Pediatric Research. 2021; 90:397-402.

Diffusion MRI Microstructural Abnormalities at Term-Equivalent Age Are Associated with Neurodevelopmental Outcomes at 3 Years of Age in Very Preterm Infants. Parikh, MN; Chen, M; Braimah, A; Kline, J; McNally, K; Logan, JW; Tamm, L; Yeates, KO; Yuan, W; He, L; et al. American Journal of Neuroradiology. 2021; 42:1535-1542.

Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. Kline, JE; Illapani, VS P; Li, H; He, L; Yuan, W; Parikh, NA. NeuroImage: Clinical. 2021; 31.

Perinatal Risk and Protective Factors in the Development of Diffuse White Matter Abnormality on Term-Equivalent Age Magnetic Resonance Imaging in Infants Born Very Preterm. Parikh, NA; Sharma, P; He, L; Li, H; Altaye, M; Priyanka Illapani, VS; Arnsperger, A; Beiersdorfer, T; Bridgewater, K; Cahill, T; et al. Journal of Pediatrics. 2021; 233:58-65.e3.

Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants. Li, H; Chen, M; Wang, J; Illapani, VS P; Parikh, NA; He, L. Radiology. Artificial intelligence.. 2021; 3.

Current and emerging artificial intelligence applications for pediatric abdominal imaging. Dillman, JR; Somasundaram, E; Brady, SL; He, L. Pediatric Radiology. 2021.

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Li, H; He, L; Dudley, JA; Maloney, TC; Somasundaram, E; Brady, SL; Parikh, NA; Dillman, JR. Pediatric Radiology. 2021; 51:392-402.

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