A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants. - Zhiyuan Li 011; Hailong Li; Adebayo Braimah; Jonathan R. Dillman; Nehal A. Parikh; Lili He 003 (NeuroImage, 2022)

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. - Junqi Wang 001; Hailong Li; Gang Qu 002; Kim M. Cecil; Jonathan R. Dillman; Nehal A. Parikh; Lili He 003 (Medical Image Anal., 2023)

A novel collaborative self-supervised learning method for radiomic data. - Zhiyuan Li 011; Hailong Li; Anca L. Ralescu; Jonathan R. Dillman; Nehal A. Parikh; Lili He 003 (NeuroImage, 2023)

Trade-Offs in Deep Learning Model Loss and Configuration for Sparse Histological Segmentation: A Case Study in Pediatric Ileal Histology. - Sixten Heekin; Oscar Lopez-Nunez; Julia Smith; Nicholas Denson; Lee Denson; Alexander Miethke; Jonathan R. Dillman; Emily Miraldi; J. Matthew Kofron; Jasbir Dhaliwal; Lauren Erdman (SIPAIM, 2024)

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment. - Zhiyuan Li 011; Hailong Li; Anca L. Ralescu; Jonathan R. Dillman; Mekibib Altaye; Kim M. Cecil; Nehal A. Parikh; Lili He 003 (Artif. Intell. Medicine, 2024)

Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. - Hailong Li; Junqi Wang 001; Zhiyuan Li 011; Kim M. Cecil; Mekibib Altaye; Jonathan R. Dillman; Nehal A. Parikh; Lili He 003 (NeuroImage, 2024)

Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset. - Shruti Hegde; Mabon Manoj Ninan; Jonathan R. Dillman; Shireen Hayatghaibi; Lynn Babcock; Elanchezhian Somasundaram (CoRR, 2025)