He Lab

Publications

Li, H; Hung, Y; Wang, J; Rudberg, N; Parikh, NA; He, L. Altered neurobehavioral white matter integrity in preterm children: A confounding-controlled analysis using the adolescent brain and cognitive development (ABCD) study. NeuroImage. 2025; 323:121600.

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. 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. J Imaging Inform Med. 2025; 38(5):2688-2699.

Lu, Z; Li, H; Parikh, NA; Dillman, JR; He, L. RadCLIP: Enhancing Radiologic Image Analysis Through Contrastive Language-Image Pretraining. IEEE Transactions on Neural Networks and Learning Systems. 2025; 36(10):17613-17622.

Derbie, AY; Tamm, L; Kline-Fath, B; Li, H; Harpster, K; Merhar, SL; He, L; Altaye, M; Parikh, NA. Diffuse white matter abnormality is independently predictive of neurodevelopmental outcomes in preterm infants. Archives of Disease in Childhood: Fetal and Neonatal Edition. 2025.

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. Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI. European Radiology. 2025; 35(7):4362-4373.

Jia, W; Li, H; Ali, R; Shanbhogue, KP; Masch, WR; Aslam, A; Harris, DT; Reeder, SB; Dillman, JR; He, L. Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data. J Imaging Inform Med. 2025; 38(2):1016-1027.

Jain, S; Fu, TT; Barnes-Davis, ME; Sahay, RD; Ehrlich, SR; Liu, C; Habli, M; Parikh, NA. Maternal Hypertension and Adverse Neurodevelopment in a Cohort of Preterm Infants. JAMA Network Open. 2025; 8(4):e257788.

Derbie, AY; Altaye, M; Wang, J; Allahverdy, A; He, L; Tamm, L; Parikh, NA. Early life brain network connectivity antecedents of executive function in children born preterm. Communications Biology. 2025; 8(1):345.

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

He, L; Li, H; Parikh, NA. MRI and Artificial Intelligence for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. In: Handbook of the Biology and Pathology of Mental Disorders. Springer Nature; 2025:1899-1922.

Joshi, A; Li, H; Parikh, NA; He, L. FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions. Frontiers in Neuroscience. 2025; 19:1734498.

Wang, J; Li, H; Cecil, KM; Altaye, M; Parikh, NA; He, L. DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants. Computer Methods and Programs in Biomedicine. 2024; 257:108479.

Li, Z; Li, H; Ralescu, AL; Dillman, JR; Altaye, M; Cecil, KM; Parikh, NA; He, L. Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment. Artificial Intelligence in Medicine. 2024; 157:102993.

Li, H; Alves, VV; Pednekar, A; Manhard, MK; Greer, J; Trout, AT; He, L; Dillman, JR. Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features. Journal of Computer Assisted Tomography. 2024; 48(6):955-962.

Li, H; Wang, J; Li, Z; Cecil, KM; Altaye, M; Dillman, JR; Parikh, NA; He, L. Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. NeuroImage. 2024; 291:120579.

Mahabee-Gittens, EM; Priyanka Illapani, VS; Merhar, SL; Kline-Fath, B; Harun, N; He, L; Parikh, NA. Prenatal Opioid Exposure and Risk for Adverse Brain and Motor Outcomes in Infants Born Premature. Journal of Pediatrics. 2024; 267:113908.

Kojima, K; Kline, JE; Altaye, M; Kline-Fath, BM; Parikh, NA. Corpus Callosum Abnormalities at Term-Equivalent Age Are Associated with Language Development at 2 Years' Corrected Age in Infants Born Very Preterm. Journal of Pediatrics: Clinical Practice. 2024; 11:200101.

Mahabee-Gittens, EM; Harun, N; Glover, M; Folger, AT; Parikh, NA. Prenatal tobacco smoke exposure and risk for cognitive delays in infants born very premature. Scientific Reports. 2024; 14(1):1397.

Liu, RX; Li, H; Towbin, AJ; Ata, NA; Smith, EA; Tkach, JA; Denson, LA; He, L; Dillman, JR. Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data. American Journal of Roentgenology. 2024; 222(1):e2329812.

Joshi, A; Li, H; Parikh, NA; He, L. A systematic review of automated methods to perform white matter tract segmentation. Frontiers in Neuroscience. 2024; 18:1376570.

Barnes-Davis, ME; Williamson, BJ; Kline, JE; Kline-Fath, BM; Tkach, J; He, L; Yuan, W; Parikh, NA. Structural connectivity at term equivalent age and language in preterm children at 2 years corrected. Brain Communications. 2024; 6(2):fcae126.

He, L; Li, H; Parikh, NA. MRI and Artificial Intelligence for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. In: Handbook of the Biology and Pathology of Mental Disorders. Springer Nature; 2024:1-24.

Li, Z; Li, H; Ralescu, AL; Dillman, JR; Altaye, M; Cecil, KM; Parikh, NA; He, L. Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment. . 2024; 157:102993-102993.

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(9).

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(8).

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. American Journal of Obstetrics and Gynecology MFM. 2023; 5(3):100856.

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(8):1618.

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

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, VSP; 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. 2022; 52(11):2139-2148.

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(4):623.e1-623.e13.

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. 2022; 52(11):2227-2240.

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(5):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(4).

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

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(3):fcac112.

Zhao, X; Peng, X; Niu, K; Li, H; He, L; Yang, F; Wu, T; Chen, D; Zhang, Q; Ouyang, M; Guo, J; Pan, Y. 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.

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; 260:119484-119484.

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.

Chandwani, R; Kline, JE; Harpster, K; Tkach, J; Parikh, NA. Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants. Human Brain Mapping. 2021; 42(14):4708-4721.

Merhar, SL; Jiang, W; Parikh, NA; Yin, W; Zhou, Z; Tkach, JA; Wang, L; Kline-Fath, BM; He, L; Braimah, A; Vannest, J; Lin, W. 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(2):397-402.

Parikh, MN; Chen, M; Braimah, A; Kline, J; Mcnally, K; Logan, JW; Tamm, L; Yeates, KO; Yuan, W; He, L; Parikh, NA. 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(8):1535-1542.

Parikh, NA; Sharma, P; He, L; Li, H; Altaye, M; Priyanka Illapani, VS. 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. Journal of Pediatrics. 2021; 233:58-65.e3.

Li, H; Chen, M; Wang, J; Illapani, VSP; 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(3):e200166.

Logan, JW; Tan, J; Skalak, M; Fathi, O; He, L; Kline, 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(3):519-527.

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. 2021; 51(3):392-402.

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: Clinical. 2021; 31:102739.

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.