Hailong Li, PhD

Hailong Li

Research Associate

Hailong Li, PhD, is a Research Associate in the lab. He received his BS degree in Automation, and MS degree in Control Theory and Engineering from Northeastern University (Shenyang, China). He earned his Ph.D. degree in Computer Science in 2013 from The University of Cincinnati (Cincinnati, OH, US). He finished a post-doctoral training in Biomedical Informatics of Cincinnati Children’s Hospital Medical Center (Cincinnati, Oh, US) in 2016. His research focuses on applying machine learning algorithms to medical image analysis. Specifically, he applied novel deep learning models (e.g., deep neural networks (DNN), deep convolutional neural networks (CNN), U-Net, and graph convolutional networks (GCN), etc.) to diagnosis and prognosis of neurodevelopment, Autism, ADHD, as well as liver fibrosis. His current studies include risk prediction in very preterm neonates using structural and functional MRI data, Autism of adolescents and adults using functional connecotme, and liver stiffness and fibrosis of adolescents using anatomical MRI abdominal images. He is familiar with advanced machine learning tools, such as TensorFlow, Keras, and Python.

Biography & Affiliation

Research Interests

Machine learning; deep learning; medical image processing and analysis

Academic Affiliation

Research Associate


Perinatal Institute


Post-doc Research Fellow

Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 2016.

Doctor of Philosophy (Ph.D.)

Computer Science and Engineering, University of Cincinnati, Cincinnati, OH, 2013.

Master of Science (M.S.)

Control Theory and Engineering, Northeastern University, China, 2007.

Bachelor of Science (B.S)

Automation, Northeastern University, China, 2004


ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome.

Introduction: Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. Our study presents a novel deep Connectome–Inception CNN (ConCeptCNN) model, which is developed based on multiple convolutional filters. The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. The ConCeptCNN uses multiple vector-shaped filters to extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of attention deficit hyperactivity disorder (ADHD) in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2 years corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders.

  • 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
  • Chen, Ming, Hailong Li, Howard Fan, Jonathan R. Dillman, Hui Wang, Mekibib Altaye, Bin Zhang, Nehal A. Parikh, and Lili He. "ConCeptCNN: A novel multi‐filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome." Medical Physics 49, no. 5 (2022): 3171-3184. Pubmed Journal
  • He L, Li H, Chen M, Wang J, Altaye M, Dillman JR, Parikh NA. (2021). Deep multimodal learning from MRI and clinical data for early prediction of neurodevelopmental deficits in very preterm infants. Front Neurosci. 15:753033. doi:10.3389/fnins.2021.753033. 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. Journal
  • 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
  • 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
  • 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
  • Chen M., Li H., Wang J., Dillman J.R., Parikh N.A., He L., (2019) A multi-channel deep neural network model analyzing multiscale functional brain connectome data for ADHD detection. Radiology, Artificial Intelligence. 2019;2(1):e190012. 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
  • Parikh MN, Li H, He L. (2019). Enhancing Diagnosis of Autism with Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci. 15, 13-9. PMID: 30828295; PMCID: PMC6384273. 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 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. Pubmed Journal