He Lab

Publications

Li, Z; Li, H; Ralescu, AL; Dillman, JR; Parikh, NA; He, L. A novel collaborative self-supervised learning method for radiomic data. Neuroimage. 2023; 277:120229.

Wang, J; Li, H; Qu, G; Cecil, KM; Dillman, JR; Parikh, NA; He, L. Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Medical Image Analysis. 2023; 87:102828.

Qian, J; Li, H; Wang, J; He, L. Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics. 2023; 13:1571.

Li, H; Li, Z; Du, K; Zhu, Y; Parikh, NA; He, L. A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants. Diagnostics. 2023; 13:1508.

Prasad, A; Dillman, J; Lubert, A; Trout, A; He, L; Li, H. PREDICTION OF FONTAN OUTCOMES USING T2-WEIGHTED MRI RADIOMIC FEATURES AND MACHINE LEARNING. Journal of the American College of Cardiology. 2023; 81:1618.

Mahabee-Gittens, EM; Kline-Fath, BM; Harun, N; Folger, AT; He, L; Parikh, NA. Prenatal tobacco smoke exposure and risk of brain abnormalities on magnetic resonance imaging at term in infants born very preterm. 2023; 5:100856.

Li, Z; Li, H; Ralescu, AL; Dillman, JR; Parikh, NA; He, L. A Novel Collaborative Self-Supervised Learning Method for Radiomic Data. 2023; abs/2302.09807.

Kline, JE; Dudley, J; Illapani, VS P; Li, H; Kline-Fath, B; Tkach, J; He, L; Yuan, W; Parikh, NA. Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology. Neuroimage. 2022; 264:119727.

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. Neuroimage. 2022; 260:119484.

Dillman, JR; Somasundaram, E; Brady, SL; He, L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatric Radiology: roentgenology, nuclear medicine, ultrasonics, CT, MRI. 2022; 52:2139-2148.

Ali, R; Li, H; Dillman, JR; Altaye, M; Wang, H; Parikh, NA; He, L. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatric Radiology: roentgenology, nuclear medicine, ultrasonics, CT, MRI. 2022; 52:2227-2240.

Jain, VG; Kline, JE; He, L; Kline-Fath, BM; Altaye, M; Muglia, LJ; DeFranco, EA; Ambalavanan, N; Parikh, NA. Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants. American Journal of Obstetrics and Gynecology. 2022; 227:623.e1-623.e13.

Zhao, X; Peng, X; Niu, K; Li, H; He, L; Yang, F; Wu, T; Chen, D; Zhang, Q; Ouyang, M; et al. A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy. Frontiers in Neuroinformatics. 2022; 16:771965.

Jiang, W; Merhar, SL; Zeng, Z; Zhu, Z; Yin, W; Zhou, Z; Wang, L; He, L; Vannest, J; Lin, W. Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks. Brain Communications. 2022; 4:fcac112.

Chen, M; Li, H; Fan, H; Dillman, JR; Wang, H; Altaye, M; Zhang, B; Parikh, NA; He, L. ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Medical Physics. 2022; 49:3171-3184.

Zhang, H; Li, H; Dillman, JR; Parikh, NA; He, L. Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks. Diagnostics. 2022; 12:816.

Guo, J; Xiao, N; Li, H; He, L; Li, Q; Wu, T; He, X; Chen, P; Chen, D; Xiang, J; et al. Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients. Frontiers in Molecular Biosciences. 2022; 9:822810.

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. Neuroimage. 2022; 260:119484.

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. 2022; abs/2202.04134.

He, L; Li, H; Chen, M; Wang, J; Altaye, M; Dillman, JR; Parikh, NA. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Frontiers in Neuroscience. 2021; 15:753033.

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

Merhar, SL; Jiang, W; Parikh, NA; Yin, W; Zhou, Z; Tkach, JA; Wang, L; Kline-Fath, BM; He, L; Braimah, A; et al. Effects of prenatal opioid exposure on functional networks in infancy. Developmental Cognitive Neuroscience. 2021; 51:100996.

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

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

Parikh, NA; Sharma, P; He, L; Li, H; Altaye, M; Priyanka Illapani, VS; Arnsperger, A; Beiersdorfer, T; Bridgewater, K; Cahill, T; 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. The Journal of Pediatrics. 2021; 233:58-65.e3.

Li, H; Chen, M; Wang, J; Illapani, VS P; Parikh, NA; He, L. Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants. Radiology. Artificial intelligence.. 2021; 3:e200166.

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

Logan, JW; Tan, J; Skalak, M; Fathi, O; He, L; Klein, J; Klebanoff, M; Parikh, NA. Adverse effects of perinatal illness severity on neurodevelopment are partially mediated by early brain abnormalities in infants born very preterm. Journal of Perinatology. 2021; 41:519-527.

Kline, JE; Illapani, VS P; Li, H; He, L; Yuan, W; Parikh, NA. Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. NeuroImage-Clinical. 2021; 31:102739.

Parikh, NA; Harpster, K; He, L; Illapani, VS P; Khalid, FC; Klebanoff, MA; O’Shea, TM; Altaye, M. Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Scientific Reports. 2020; 10:15920.

He, L; Li, H; Wang, J; Chen, M; Gozdas, E; Dillman, JR; Parikh, NA. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Scientific Reports. 2020; 10:15072.

Chen, M; Li, H; Wang, J; Yuan, W; Altaye, M; Parikh, NA; He, L. Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Frontiers in Neuroscience. 2020; 14:858.

Kline, JE; Illapani, VS P; He, L; Altaye, M; Logan, JW; Parikh, NA. Early cortical maturation predicts neurodevelopment in very preterm infants. Archives of Disease in Childhood: Fetal and Neonatal Edition. 2020; 105:460-465.

Parikh, NA; He, L; Li, H; Illapani, VS P; Klebanoff, MA. Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatric Neurology. 2020; 106:56-62.

Parikh, NA; He, L; Illapani, VS P; 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. The Journal of Pediatrics. 2020; 220:56-63.

Merhar, SL; Gozdas, E; Tkach, JA; Parikh, NA; Kline-Fath, BM; He, L; Yuan, W; Altaye, M; Leach, JL; Holland, SK. Neonatal Functional and Structural Connectivity Are Associated with Cerebral Palsy at Two Years of Age. American Journal of Perinatology: neonatal and maternal-fetal medicine. 2020; 37:137-145.

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. Radiology. Artificial intelligence.. 2020; 2:e190012.

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-Clinical. 2020; 28:102475.

Kline, JE; Illapani, VS P; He, L; Altaye, M; Parikh, NA. Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Scientific Reports. 2019; 9:19679.

He, L; Li, H; Dudley, JA; Maloney, TC; Brady, SL; Somasundaram, E; Trout, AT; Dillman, JR. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. American Journal of Roentgenology. 2019; 213:592-601.

Li, H; Parikh, NA; Wang, J; Merhar, S; Chen, M; Parikh, M; Holland, S; He, L. Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Frontiers in Neuroscience. 2019; 13:610.

Parikh, MN; Li, H; He, L. Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Frontiers in Computational Neuroscience. 2019; 13:9.