Kevin P. Nguyen
Orcid: 0000-0001-5520-7285
According to our database1,
Kevin P. Nguyen
authored at least 13 papers
between 2019 and 2024.
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Bibliography
2024
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study.
CoRR, 2024
2023
Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data.
IEEE Trans. Pattern Anal. Mach. Intell., July, 2023
BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping.
Brain Connect., March, 2023
2022
Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.
Neuroinformatics, 2022
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data.
CoRR, 2022
Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data.
CoRR, 2022
2020
Anatomically informed data augmentation for functional MRI with applications to deep learning.
Proceedings of the Medical Imaging 2020: Image Processing, 2020
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
2019
Proceedings of the Predictive Intelligence in Medicine - Second International Workshop, 2019
Sensitivity of Derived Clinical Biomarkers to rs-fMRI Preprocessing Software Versions.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019
Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted From Structural And Functional Mri.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019