Gabrielle Dagasso

According to our database, Gabrielle Dagasso authored at least 8 papers between 2020 and 2025.

Collaborative distances:

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

2025

Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis.

Raissa Souza, Emma A. M. Stanley, Anthony J. Winder, Chris Kang, Kimberly Amador, Erik Y. Ohara, Gabrielle Dagasso, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

npj Digit. Medicine, 2025

Accounting for population structure in deep learning models for genomic analysis.

Gabrielle Dagasso, Matthias Wilms, Raissa Souza, Nils D. Forkert

J. Biomed. Informatics, 2025

2024

Application of a localized morphometrics approach to imaging-derived brain phenotypes for genotype-phenotype associations in pediatric mental health and neurodevelopmental disorders.

Gabrielle Dagasso, Matthias Wilms, Sarah J. MacEachern, Nils D. Forkert

Frontiers Big Data, 2024

A Lightweight 3D Conditional Diffusion Model for Self-explainable Brain Age Prediction in Adults and Children.

Matthias Wilms, Ahmad O. Ahsan, Erik Y. Ohara, Gabrielle Dagasso, Elizabeth Macavoy, Emma A. M. Stanley, Vibujithan Vigneshwaran, Nils D. Forkert

Proceedings of the Machine Learning in Clinical Neuroimaging - 7th International Workshop, 2024

Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis.

Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Matthias Wilms, G. Bruce Pike, Gabrielle Dagasso, Christopher Nielsen, Sarah J. MacEachern, Nils D. Forkert

Proceedings of the Ethics and Fairness in Medical Imaging, 2024

2022

A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies.

Gabrielle Dagasso, Matthias Wilms, Nils D. Forkert

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021

Leveraging machine learning to advance genome-wide association studies.

Gabrielle Dagasso, Yan Yan, Lipu Wang, Longhai Li, Randy Kutcher, Wentao Zhang, Lingling Jin

Int. J. Data Min. Bioinform., 2021

2020

Comprehensive-GWAS: a pipeline for genome-wide association studies utilizing cross-validation to assess the predictivity of genetic variations.

Gabrielle Dagasso, Yan Yan, Lipu Wang, Longhai Li, Randy Kutcher, Wentao Zhang, Lingling Jin

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020