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


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.

  • Redha Ali, Hailong Li, Jonathan R. Dillman, Mekibib Altaye, Hui Wang, Nehal A. Parikh, and Lili He. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatric Radiology. Pubmed - Accepted Journal - Accepted
  • Li Z, Li H, Braimah A, Dillman JR, Parikh NA, He L. A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants [published online ahead of print, 2022 Jul 15]. Neuroimage. 2022;260:119484. doi:10.1016/j.neuroimage.2022.119484. Pubmed Journal
  • Jain VG, Kline JE, He L, et al. Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants [published online ahead of print, 2022 May 26]. Am J Obstet Gynecol. 2022;S0002-9378(22)00393-3. doi:10.1016/j.ajog.2022.05.042. Pubmed Journal
  • Chen M, Li H, Fan H, et al. ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Med Phys. 2022;49(5):3171-3184. doi:10.1002/mp.15545. Pubmed Journal
  • Zhang H, Li H, Dillman JR, Parikh NA, He L. Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks. Diagnostics (Basel). 2022;12(4):816. Published 2022 Mar 26. doi:10.3390/diagnostics12040816. Pubmed Journal
  • Guo J, Xiao N, Li H, et al. Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients. Front Mol Biosci. 2022;9:822810. Published 2022 Mar 4. doi:10.3389/fmolb.2022.822810. Pubmed Journal
  • Kline JE, Illapani VSP, Li H, He L, Yuan W, Parikh NA. Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. Neuroimage Clin. 2021;31:102739. doi:10.1016/j.nicl.2021.102739. Pubmed Journal
  • He L, Li H, Chen M, et al. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Front Neurosci. 2021;15:753033. Published 2021 Oct 5. doi:10.3389/fnins.2021.753033. Pubmed Journal
  • Merhar SL, Jiang W, Parikh NA, et al. Effects of prenatal opioid exposure on functional networks in infancy. Dev Cogn Neurosci. 2021;51:100996. doi:10.1016/j.dcn.2021.100996. Pubmed Journal
  • Parikh MN, Chen M, Braimah A, Kline J, McNally K, Logan JW, Tamm L, Yeates KO, Yuan W, He L, Parikh NA. (2021). Diffusion MRI microstructural abnormalities at term-equivalent age are associated with neurodevelopmental outcomes at 3 years of age in very preterm infants. Dev Cogn Neurosci. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A7135. Pubmed Journal
  • Li H, Chen M, Wang J, Illapani VSP, 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. 3(3). doi: 10.1148/ryai.2021200166. Pubmed Journal
  • Dillman JR, Somasundaram E, Brady SL, He L. (2021). Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol. doi:10.1007/s00247-021-05057-0. Pubmed Journal
  • Parikh NA, Sharma P, He L, et al. 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. 2021;233:58-65.e3. doi:10.1016/j.jpeds.2020.11.058. Pubmed Journal
  • Merhar SL, Kline JE, Braimah A, et al. Correction: Prenatal opioid exposure is associated with smaller brain volumes in multiple regions. Pediatr Res. 2021;90(2):493. doi:10.1038/s41390-020-01297-2. Pubmed - corrected Journal - corrected
  • Kline JE, Sita Priyanka Illapani V, He L, Parikh NA. Automated brain morphometric biomarkers from MRI at term predict motor development in very preterm infants. Neuroimage Clin. 2020;28:102475. doi:10.1016/j.nicl.2020.102475. Pubmed 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, Li H, Priyanka Illapani VS, Klebanoff MA. Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatr Neurol. 2020;106:56-62. doi:10.1016/j.pediatrneurol.2020.01.011. Pubmed Journal
  • Parikh NA, Harpster K, He L, et al. Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Sci Rep. 2020;10(1):15920. Published 2020 Sep 28. doi:10.1038/s41598-020-72632-0. Pubmed Journal
  • Parikh NA, He L, Priyanka Illapani VS, Altaye M, Folger AT, Yeates KO. Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. J Pediatr. 2020;220:56-63. doi:10.1016/j.jpeds.2020.01.034. 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, et al. Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Front Neurosci. 2020;14:858. Published 2020 Sep 18. doi:10.3389/fnins.2020.00858. Pubmed Journal
  • Li H, He L, Dudley JA, et al. 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. 2021;51(3):392-402. doi:10.1007/s00247-020-04854-3. Pubmed Journal
  • He L, Li H, Wang J, et al. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep. 2020;10(1):15072. Published 2020 Sep 15. doi:10.1038/s41598-020-71914-x. Pubmed Journal
  • Kline JE, Illapani VSP, He L, Altaye M, Logan JW, Parikh NA. Early cortical maturation predicts neurodevelopment in very preterm infants. Arch Dis Child Fetal Neonatal Ed. 2020;105(5):460-465. doi:10.1136/archdischild-2019-317466. Pubmed Journal
  • Kline JE, Illapani VSP, He L, Altaye M, Parikh NA. Author Correction: Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Sci Rep. 2021;11(1):7055. Published 2021 Mar 23. doi:10.1038/s41598-021-86025-4. Pubmed - corrected Journal - corrected
  • Chen M, Li H, Wang J, Dillman JR, Parikh NA, He L. A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. Radiol Artif Intell. 2019;2(1):e190012. Published 2019 Dec 11. doi:10.1148/ryai.2019190012. Pubmed Journal
  • Li H, Parikh NA, Wang J, et al. Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Front Neurosci. 2019;13:610. Published 2019 Jun 18. doi:10.3389/fnins.2019.00610. Pubmed Journal
  • He L, Li H, Dudley JA, et al. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. AJR Am J Roentgenol. 2019;213(3):592-601. doi:10.2214/AJR.19.21082. Pubmed Journal
  • Parikh MN, Li H, He L. Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci. 2019;13:9. Published 2019 Feb 15. doi:10.3389/fncom.2019.00009. Pubmed Journal
  • Merhar SL, Gozdas E, Tkach JA, et al. Neonatal Functional and Structural Connectivity Are Associated with Cerebral Palsy at Two Years of Age. Am J Perinatol. 2020;37(2):137-145. doi:10.1055/s-0039-1683874. Pubmed Journal
  • 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. Published 2018 Jul 24. doi:10.3389/fnins.2018.00491. Pubmed Journal
  • He L, Wang J, Lu ZL, Kline-Fath BM, Parikh NA. Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. Pediatr Radiol. 2018;48(8):1139-1151. doi:10.1007/s00247-018-4140-x. Pubmed Journal
  • 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 Clin. 2018;18:290-297. Published 2018 Jan 31. doi:10.1016/j.nicl.2018.01.032. Pubmed Journal
  • Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, Holland SK. Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?. Brain Struct Funct. 2018;223(8):3665-3680. doi:10.1007/s00429-018-1707-0. Pubmed Journal
  • Wang J, He L, Zheng H, Lu ZL. Improving structural brain images acquired with the 3D FLASH sequence. Magn Reson Imaging. 2017;38:224-232. doi:10.1016/j.mri.2017.01.014. Pubmed Journal
  • He L, Parikh NA. Brain functional network connectivity development in very preterm infants: The first six months. Early Hum Dev. 2016;98:29-35. doi:10.1016/j.earlhumdev.2016.06.002. Pubmed Journal
  • He L, Parikh NA. Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities. Pediatr Neurol. 2015;53(4):330-337. doi:10.1016/j.pediatrneurol.2015.05.001. Pubmed Journal
  • Wang J, He L, Zheng H, Lu ZL. Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence. PLoS One. 2014;9(5):e96899. Published 2014 May 30. doi:10.1371/journal.pone.0096899. Pubmed Journal
  • 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. Published 2014 Jan 24. doi:10.1371/journal.pone.0085807. Pubmed Journal
  • Parikh NA, He L, Bonfante-Mejia E, et al. Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants. Pediatr Neurol. 2013;49(6):424-430. doi:10.1016/j.pediatrneurol.2013.08.026. Pubmed Journal
  • 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. Published 2013 Dec 31. doi:10.1371/journal.pone.0085475. Pubmed Journal
  • 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. doi:10.1016/j.neuroimage.2012.08.081. Pubmed Journal
  • He L, Orten B, Do S, et al. A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans Med Imaging. 2010;29(5):1182-1191. doi:10.1109/TMI.2010.2043536. Pubmed Journal
  • He L, Greenshields IR. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging. 2009;28(2):165-172. doi:10.1109/TMI.2008.927338. 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