A photo of Lili He.

Lili He, PhD


  • Associate Professor, UC Department of Radiology

About

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 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.

Interests

Machine learning; deep learning; medical image processing and analysis

Publications

Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI. Ali, R; Li, H; Zhang, H; Pan, W; Reeder, SB; Harris, D; Masch, W; Aslam, A; Shanbhogue, K; Bernieh, A; Ranganathan, S; Parikh, N; Dillman, JR; He, L. European Radiology. 2025; 35:4362-4373.

RadCLIP: Enhancing Radiologic Image Analysis Through Contrastive Language-Image Pretraining. Lu, Z; Li, H; Parikh, NA; Dillman, JR; He, L. Journal of Central South University. 2025; PP:1-10.

Maternal Hypertension and Adverse Neurodevelopment in a Cohort of Preterm Infants. Jain, S; Ting Fu, T; Barnes-Davis, ME; Sahay, RD; Ehrlich, SR; Liu, C; Habli, M; Parikh, NA. JAMA Network Open. 2025; 8:e257788.

Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data. Jia, W; Li, H; Ali, R; Shanbhogue, KP; Masch, WR; Aslam, A; Harris, DT; Reeder, SB; Dillman, JR; He, L. 2025; 38:1016-1027.

Role of Complement in the Development of Hypertensive Nephropathy. Wang, Z; Zhang, T; Wang, X; Zhai, J; He, L; Zuo, Q; Ma, S; Zhang, G; Guo, Y. 2025; 40:308-312.

Canagliflozin ameliorates ferritinophagy in HFpEF rats. Ma, S; Zuo, QJ; He, LL; Zhang, GR; Zhang, TT; Wang, ZL; Zhai, JL; Guo, YF. Journal of geriatric cardiology : JGC. 2025; 22:178-189.

Development and Validation of a Modality-Invariant 3D Swin U-Net Transformer for Liver and Spleen Segmentation on Multi-Site Clinical Bi-parametric MR Images. Zhang, H; Li, H; Ali, R; Jia, W; Pan, W; Reeder, SB; Harris, D; Masch, W; Aslam, A; Shanbhogue, K; Parikh, NA; Dillman, JR; He, L. 2025; 1-12.

Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults. Shabanian, M; Taylor, Z; Woods, C; Bernieh, A; Dillman, J; He, L; Ranganathan, S; Picarsic, J; Somasundaram, E. Journal of Pathology Informatics. 2025; 16:100416.

Canagliflozin attenuates kidney injury, gut-derived toxins, and gut microbiota imbalance in high-salt diet-fed Dahl salt-sensitive rats. He, L; Zuo, Q; Ma, S; Zhang, G; Wang, Z; Zhang, T; Zhai, J; Guo, Y. Renal Failure. 2024; 46:2300314.

Association between circulatory complement activation and hypertensive renal damage: a case-control study. Wang, Z; Zhang, T; Wang, X; Zhai, J; He, L; Ma, S; Zuo, Q; Zhang, G; Guo, Y. Renal Failure. 2024; 46:2365396.

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