Lili He, PhD



Lili He

Associate Professor



I am a computer scientist with expertise in artificial intelligence and medical imaging. My long-standing career goal is to develop and validate robust clinically-effective AI diagnostic/ prognostic systems for physicians to use to improve diagnosis/ prediction and prevention of patient outcomes for high-risk infants and children. I have led multiple NIH- and institution- funded studies to develop MRI prognostic biomarkers and deep learning models for early detection/ prediction of various important clinical outcomes, including cognitive, language, and motor deficits, attention deficit hyperactivity disorder, autism spectrum disorder, and chronic liver diseases. I am driving the clinical translation and implementation of AI technologies in context of improved value of medical imaging (improved outcomes associated with a lowering of healthcare costs). Learn More

Biography & Affiliation

Research Interests

Machine learning; deep learning; medical image processing and analysis

Education

Post-doc Research

Massachusetts General Hospital, Harvard Medical School, Boston, MA, 2010.

Doctor of Philosophy (Ph.D.)

Computer Science and Engineering, University of Connecticut, Storrs, CT, 2008.

Master of Science (M.S.)

Computer Science, University of Missouri, Columbia, MO, 2003.

Bachelor of Science (B.S)

Electrical Engineering, Tsinghua University, Beijing, China, 1998.

Publications
  • Li H, Chen M, Wang J, Illapani V, Parikh NA, and He L. (2021) Automatic segmentation of diffuse white matter abnormality on T2-weighted brain MRI using deep learning in very preterm infants. Radiol Artif Intell (in press).
  • Parikh NA, Sharma P, He L, Li H, Altaye M, Priyanka Illapani VS. (2020) 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. J Pediatr. doi:10.1016/j.jpeds.2020.11.058, PMID: 33259857. Pubmed Journal
  • Merhar SL, Kline JE, Braimah A, Kline-Fath BM, Tkach JA, Altaye M, He L, Parikh NA. (2020) Prenatal opioid exposure is associated with smaller brain volumes in multiple regions. Pediatr Res. doi:10.1038/s41390-020-01265-w, PMID: 33177677. Pubmed Journal
  • Kline JE, Sita Priyanka Illapani V, He L, Parikh NA. (2020) Automated brain morphometric biomarkers from MRI at term predict motor development in very preterm infants. Neuroimage Clin; 28:102475. doi:10.1016/j.nicl.2020.102475, PMID: 33395969; PMCID: PMC7649646. Pubmed PMC Journal
  • Parikh NA, Harpster K, He L, Illapani VSP, Khalid FC, Klebanoff MA, O'Shea TM, Altaye M. (2020) Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Sci Rep; 10:15920. doi:10.1038/s41598-020-72632-0, PMID: 32985533; PMCID: PMC7523012. Pubmed PMC Journal
  • Alom MZ, He L, Taha TM, Asari VK. (2020). Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network. SPIE (11511), Applications of Machine Learning 2020, 115110F. Journal
  • Parikh NA, He L, Priyanka Illapani VS, Altaye M, Folger AT, & Yeates KO. (2020). Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. J Pediatr, 220, 56-63. PMID: 32147220. Pubmed Journal
  • Logan JW, Tan J, Skalak M, Fathi O, He L, Kline J, Klebanoff M, Parikh NA. (2020) Adverse effects of perinatal illness severity on neurodevelopment are partially mediated by early brain abnormalities in infants born very preterm. J Perinatol. doi:10.1038/s41372-020-00854-1, PMID: 33028936. Pubmed Journal
  • Chen M, Li H, Wang J, Yuan W, Altaye M, Parikh NA and He L (2020) Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Front. Neurosci. 14:858. doi: 10.3389/fnins.2020.00858 Journal
  • Li H, He L, Dudley JA, Maloney TC, Somasundaram E, Brady SL, Parikh NA, Dillman JR. (2020) DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Pediatr Radiol. doi:10.1007/s00247-020-04854-3, PMID: 33048183. Pubmed Journal
  • He L, Li H, Wang J, Chen M, Gozdas, E, Dillman JR, Parikh NA, (2020). A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep 10, 15072 (2020). PMCID: PMC7492237 PMC Journal
  • Julia E. Kline, Venkata Sita Priyanka Illapani, Hailong Li, Lili He, Nehal A. Parikh, (2020). Diffuse white matter abnormality in very preterm infants reflects reduced brain network efficiency, medRxiv, preprint. Journal
  • Parikh NA, He L, Illapani VSP, Altaye M, Folger AT, Yeates KO., (2020) Objectively-Diagnosed Diffuse White Matter Abnormality at Term is an Independent Predictor of Cognitive and Language Outcomes in Very Preterm. Journal of Pediatrics. In press. PMC Journal
  • Parikh NA, He L, Li H, Priyanka Illapani VS, & Klebanoff MA. (2020). Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatr Neurol, 106, 56-62. PMID: 32139164. Pubmed Journal
  • Parikh MN, Li H, & He L. (2019). Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci, 13, 9. PMID: 30828295; PMCID: PMC6384273. PMC Journal
  • Kline JE, Illapani VSP, He L, Altaye M, Logan JW, & Parikh NA. (2019). Early cortical maturation predicts neurodevelopment in very preterm infants. Arch Dis Child Fetal Neonatal Ed. PMID: 31704737; PMCID: PMC7205568. Pubmed Journal
  • Kline JE, Illapani VSP, He L, Altaye M, & Parikh NA. (2019). Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Sci Rep, 9(1), 19679. PMID: 31873183; PMCID: PMC6928014. PMC Journal
  • Chen M, Li H, Wang J, Dillman JR, Parikh NA, & He L. (2019). A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. Radiology: Artificial Intelligence, 2(1), e190012. Doi: 10.1148/ryai.2019190012 PMC Journal
  • Li H., Parikh N.A., Wang J., Merhar S., Chen M., Parikh M., Holland S., He L., (2019). Objective and automated detection of diffuse white matter abnormality in preterm infants using deep convolutional neural networks. Frontiers in Neuroscience, in press. PMC Journal
  • He L., Li H, Dudley JA, Maloney TC, Brady SL, Somasundaram E, Trout AT and Dillman JR. (2019) Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. American Journal of Roentgenology, 1-10. 10.2214/AJR.19.21082. PMID:31120779 Pubmed Journal
  • Merhar SL, Gozdas E, Tkach JA, Parikh NA, Kline-Fath BM, He L, Yuan W, Altaye M, Leach JL, & Holland SK. (2019). Neonatal Functional and Structural Connectivity Are Associated with Cerebral Palsy at Two Years of Age. Am J Perinatol. PMID: 30919395. Pubmed Journal
  • Li H, Parikh NA, & He L. (2018). A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci, 12, 491. PMID: 30087587; PMCID: PMC6066582. Pubmed PMC Journal
  • He L, Wang J, Lu ZL, Kline-Fath BM, & Parikh NA. (2018). Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. Pediatr Radiol, 48(8), 1139-1151. PMID: 29721599; PMCID: PMC6148771. Pubmed PMC Journal
  • He L, Li H, Holland SK, Yuan W, Altaye M, & Parikh NA. (2018). Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. Neuroimage Clin, 18, 290-297. PMID: 29876249; PMCID: PMC5987842. PMC Journal
  • Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, & Holland SK. (2018). Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments? Brain Struct Funct. PMID: 29992470. Pubmed PMC Journal
  • Wang J, He L, Zheng H, & Lu ZL. (2017). Improving structural brain images acquired with the 3D FLASH sequence. Magn Reson Imaging, 38, 224-232. PMID: 28109888. Pubmed Journal
  • He L, & Parikh NA. (2016). Brain functional network connectivity development in very preterm infants: The first six months. Early Hum Dev, 98, 29-35. PMID: 27351350. Pubmed Journal
  • He L, & Parikh NA. (2015). Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities. Pediatr Neurol, 53(4), 330-337. PMID: 26216502. Pubmed Journal
  • Wang J, He L, Zheng H, & Lu ZL. (2014). Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence. PLoS One, 9(5), e96899. PMID: 24879508; PMCID: PMC4039442. Pubmed Journal
  • Kaur S, Powell S, He L, Pierson CR, & Parikh NA. (2014). Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age. PLoS One, 9(1), e85807. PMID: 24475054; PMCID: PMC3901659. PMC Journal
  • Parikh NA, He L, Bonfante-Mejia E, Hochhauser L, Wilder PE, Burson K, & Kaur S. (2013). Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants. Pediatr Neurol, 49(6), 424-430. PMID: 24138952; PMCID: PMC3957176. PMC Journal
  • He L, & Parikh NA. (2013). 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, 8(12), e85475. PMID: 24392012; PMCID: PMC3877364. Pubmed Journal
  • He L, & Parikh NA. (2013). Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants. Neuroimage, 64, 328-340. PMID: 22974556; PMCID: PMC3544934. Pubmed PMC Journal
  • He L, Orten B, Do S, Karl WC, Kambadakone A, Sahani DV, & Pien H. (2010). A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans Med Imaging, 29(5), 1182-1191. PMID: 20378468. Journal
  • He L, & Greenshields IR. (2009). A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging, 28(2), 165-172. PMID: 19188105. Pubmed Journal
  • He L, & Greenshields IR. (2008). An MRF spatial fuzzy clustering method for fMRI SPMs. Biomedical Signal Processing and Control, 3(4), 327-333. Journal