Richard Camicioli

According to our database, Richard Camicioli authored at least 10 papers between 2010 and 2026.

Collaborative distances:

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

2026

Combining federated learning and travelling model boosts performance and opens opportunities for digital health equity.

Raissa Souza, Emma A. M. Stanley, Erik Y. Ohara, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

npj Digit. Medicine, 2026

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

2024

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Raissa Souza, Anthony J. Winder, Emma A. M. Stanley, Vibujithan Vigneshwaran, Milton Camacho, Richard Camicioli, Oury Monchi, Matthias Wilms, Nils D. Forkert

IEEE J. Biomed. Health Informatics, April, 2024

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Raissa Souza, Emma A. M. Stanley, Milton Camacho, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

Frontiers Artif. Intell., 2024

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.

Kauê T. N. Duarte, Abhijot S. Sidhu, Murilo Costa de Barros, David G. Gobbi, Cheryl R. McCreary, Feryal Saad, Richard Camicioli, Eric E. Smith, Mariana P. Bento, Richard Frayne

Frontiers Comput. Neurosci., 2024

Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis?

Raissa Souza, Emma A. M. Stanley, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

Proceedings of the Ethics and Fairness in Medical Imaging, 2024

2023

Segmenting white matter hyperintensities in brain magnetic resonance images using convolution neural networks.

Kauê T. N. Duarte, David G. Gobbi, Abhijot S. Sidhu, Cheryl R. McCreary, Feryal Saad, Richard Camicioli, Eric E. Smith, Richard Frayne

Pattern Recognit. Lett., November, 2023

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data.

Raissa Souza, Matthias Wilms, Milton Camacho, G. Bruce Pike, Richard Camicioli, Oury Monchi, Nils D. Forkert

J. Am. Medical Informatics Assoc., November, 2023

2012

Diffusion tensor imaging of white matter tract evolution over the lifespan.

Catherine Lebel, Myrlene Gee, Richard Camicioli, Marguerite Wieler, Wayne Martin, Christian Beaulieu

NeuroImage, 2012

2010

Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study.

Stijn Michielse, Nick Coupland, Richard Camicioli, Rawle Carter, Peter Seres, Jennifer Sabino, Nikolai Malykhin

NeuroImage, 2010