Raissa Souza

According to our database, Raissa Souza authored at least 14 papers between 2021 and 2024.

Collaborative distances:

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

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

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert

J. Am. Medical Informatics Assoc., 2024

Foundation model-driven distributed learning for enhanced retinal age prediction.

Christopher Nielsen, Raissa Souza, Matthias Wilms, Nils D. Forkert

J. Am. Medical Informatics Assoc., 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

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

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

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

Towards objective and systematic evaluation of bias in medical imaging AI.

Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert

CoRR, 2023

Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference.

Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho, Raissa Souza, Nils D. Forkert

Proceedings of the Medical Imaging with Deep Learning, 2023

An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets.

Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert

Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, 2023

On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity.

Raissa Souza, Emma A. M. Stanley, Nils D. Forkert

Proceedings of the Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 2023

2022

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert

J. Am. Medical Informatics Assoc., 2022

2021

Federated Learning Using Variable Local Training for Brain Tumor Segmentation.

Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets.

Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021