Supervised Contrastive Learning Enhances Graph Convolutional Networks for Predicting Neurodevelopmental Deficits in Very Preterm Infants using Brain Structural Connectome. Li, H; Wang, J; Li, Z; Cecil, KM; Altaye, M; Dillman, JR; Parikh, NA; He, L. NeuroImage. 2024; 120579. Github
Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Wang, J; Li, H; Qu, G; Cecil, KM; Dillman, JR; Parikh, NA; He, L. Medical Image Analysis. 2023; 87:102828. Github
A Novel Collaborative Self-Supervised Learning Method for Radiomic Data. Li, Z; Li, H; Ralescu, AL; Dillman, JR; Parikh, NA; He, L. NeuroImage. 2023; 120229. Github
A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants. Li, H; Li, Z; Du, K; Zhu, Y; Parikh, NA; He, L. Diagnostics. 2023; 13(8): 1508. Github
A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Ali R, Li H, Dillman JR, Altaye M, Wang H, Parikh NA, He L. Pediatr Radiol. 2022 Oct;52(11):2227-2240. Epub 2022 Sep 22. PMID: 36131030; PMCID: PMC9574648. Github
A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants. Li, Z; Li, H; Braimah, A; Dillman, JR; Parikh, NA; He, L. NeuroImage. 2022; 260:119484. Github
ConCeptCNN: A novel multi‐filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Chen, M; Li, H; Fan, H; Dillman, JR; Wang, H; Altaye, M; Zhang, B; Parikh, NA; He, L. Medical physics. 2022; 49(5):3171-3184. Github
Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks. Zhang, H; Li, H; Dillman, JR; Parikh, NA; He, L. Diagnostics. 2022; 12(4):816. Github
Lili He, Hailong Li, Ming Chen, Jinghua Wang, Mekibib Altaye, Jonathan R Dillman, Nehal A Parikh. (2021). Deep multimodal learning from MRI and clinical data for early prediction of neurodevelopmental deficits in very preterm infants. Frontiers in neuroscience. Journal Github
Zhang H, Li H, Dillman, JR, Parikh, NA, He L. (2022). Multi-contrast MRI image synthesis using switchable cycle-consistent generative adversarial networks. Diagnostics. 12(4), 816. doi: 10.3390/diagnostics12040816. Journal Github
Chen M, Li H, Fan H, Dillman JR, Wang H, Altaye M, Zhang B, Parikh NA, He L. (2022). ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Medical Physics. doi: 10.1002/mp.15545. PMID: 35246986. PubMed Journal Github
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 Github
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. 2020 Oct 13. doi: 10.1007/s00247-020-04854-3. Epub ahead of print. PMID: 33048183. PubMed Journal Github
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. (2020). Sci Rep. 15;10(1):15072. PMID: 32934282; PMCID: PMC749223 PubMed Journal Github
Chen M, Li H, Wang J, Yuan W, Altaye M, Parikh NA, 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, 18;14:858. PMID: 33041749; PMCID: PMC7530168. PubMed Journal Github
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. Radiol Artif Intell. 11;2(1):e190012. doi: 10.1148/ryai.2019190012. PMID: 32076663; PMCID: PMC6996597. PMC Journal Github
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, PMCID: PMC6591530 PMID: 31275101 PMC Journal Github
He L., Li H., Dudley JA., Maloney TC., Brady SL., Somasundaram E., Trout AT, Dillman JR., (2019). Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. AJR Am J Roentgenol. 2019 Sep;213(3):592-601. doi: 10.2214/AJR.19.21082. Epub 2019 May 23. PMID: 31120779. PubMed Journal Github
Parikh MN., Li H., He L., (2019). Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci. 2019 Feb 15;13:9. doi: 10.3389/fncom.2019.00009. PMID: 30828295; PMCID: PMC6384273. PubMed Journal Github
Li H., Parikh NA., He L., (2018). A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci. 2018 Jul 24;12:491. doi: 10.3389/fnins.2018.00491. PMID: 30087587; PMCID: PMC6066582. PubMed Journal Github
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. 2018 Jan 31;18:290-297. doi: 10.1016/j.nicl.2018.01.032. PMID: 29876249; PMCID: PMC5987842. PubMed Journal Github