Jonathan R. Dillman

According to our database, Jonathan R. Dillman authored at least 11 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

RadCLIP: Enhancing Radiologic Image Analysis Through Contrastive Language-Image Pretraining.

Zhixiu Lu, Hailong Li, Nehal A. Parikh, Jonathan R. Dillman, Lili He

IEEE Trans. Neural Networks Learn. Syst., October, 2025

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, May, 2025

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.

Huixian Zhang, Hailong Li, Redha Ali, Wei Jia, Wen Pan, Scott B. Reeder, David Harris, William R. Masch, Anum Aslam, Krishna Shanbhogue, Nehal A. Parikh, Jonathan R. Dillman, Lili He

J. Imaging Inform. Medicine, 2025

Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.

Wei Jia, Hailong Li, Redha Ali, Krishna P. Shanbhogue, William R. Masch, Anum Aslam, David T. Harris, Scott B. Reeder, Jonathan R. Dillman, Lili He

J. Imaging Inform. Medicine, 2025

Comparative methods for histology-based prediction of anti-TNFα response in pediatric Crohn's disease.

Sixten Heekin, Nicholas Denson, Julia Smith, Ilyssa Gordon, Lee Denson, Jonathan R. Dillman, David Bruining, Florian Reider, Jasbir Dhaliwal, Lauren Erdman

Proceedings of the 21st International Symposium on Biomedical Image Processing and Analysis, 2025

2024

Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome.

Hailong Li, Junqi Wang, Zhiyuan Li, Kim M. Cecil, Mekibib Altaye, Jonathan R. Dillman, Nehal A. Parikh, Lili He

NeuroImage, 2024

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

Zhiyuan Li, Hailong Li, Anca L. Ralescu, Jonathan R. Dillman, Mekibib Altaye, Kim M. Cecil, Nehal A. Parikh, Lili He

Artif. Intell. Medicine, 2024

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

Proceedings of the 20th International Symposium on Medical Information Processing and Analysis, 2024

2023

A novel collaborative self-supervised learning method for radiomic data.

Zhiyuan Li, Hailong Li, Anca L. Ralescu, Jonathan R. Dillman, Nehal A. Parikh, Lili He

NeuroImage, August, 2023

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis.

Junqi Wang, Hailong Li, Gang Qu, Kim M. Cecil, Jonathan R. Dillman, Nehal A. Parikh, Lili He

Medical Image Anal., 2023

2022

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, Hailong Li, Adebayo Braimah, Jonathan R. Dillman, Nehal A. Parikh, Lili He

NeuroImage, 2022