Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. - Raissa Souza; Anup Tuladhar; Pauline Mouches; Matthias Wilms; Lakshay Tyagi; Nils D. Forkert (BrainLes@MICCAI (2), 2021)

Federated Learning Using Variable Local Training for Brain Tumor Segmentation. - Anup Tuladhar; Lakshay Tyagi; Raissa Souza; Nils D. Forkert (BrainLes@MICCAI (2), 2021)

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)

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 (CLIP/FAIMI/EPIMI@MICCAI, 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 (Computer-Aided Diagnosis, 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 (MIDL, 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)

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., 2023)

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 (FAIMI/EPIMI@MICCAI, 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 (FAIMI/EPIMI@MICCAI, 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)

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)

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)

Distributed Deep Learning Methods for Medical Imaging Analysis. - Raissa Souza (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, 2024)

Accounting for population structure in deep learning models for genomic analysis. - Gabrielle Dagasso; Matthias Wilms; Raissa Souza; Nils D. Forkert (J. Biomed. Informatics, 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)