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Raissa Souza
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2020 – today
- 2024
- [j6]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. Frontiers Artif. Intell. 7 (2024) - [j5]Christopher Nielsen, Raissa Souza, Matthias Wilms, Nils D. Forkert:
Foundation model-driven distributed learning for enhanced retinal age prediction. J. Am. Medical Informatics Assoc. 31(11): 2550-2559 (2024) - [j4]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert:
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. J. Am. Medical Informatics Assoc. 31(11): 2613-2621 (2024) - [j3]Raissa Souza, Anthony J. Winder, Emma A. M. Stanley, Vibujithan Vigneshwaran, Milton Camacho, Richard Camicioli, Oury Monchi, Matthias Wilms, Nils D. Forkert:
Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? IEEE J. Biomed. Health Informatics 28(4): 2047-2054 (2024) - [c7]Raissa Souza, Emma A. M. Stanley, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? FAIMI/EPIMI@MICCAI 2024: 119-128 - [c6]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Matthias Wilms, G. Bruce Pike, Gabrielle Dagasso, Christopher Nielsen, Sarah J. MacEachern, Nils D. Forkert:
Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. FAIMI/EPIMI@MICCAI 2024: 163-175 - 2023
- [j2]Raissa Souza, Matthias Wilms, Milton Camacho, G. Bruce Pike, Richard Camicioli, Oury Monchi, Nils D. Forkert:
Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J. Am. Medical Informatics Assoc. 30(12): 1925-1933 (2023) - [c5]Raissa Souza, Emma A. M. Stanley, Nils D. Forkert:
On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. CLIP/FAIMI/EPIMI@MICCAI 2023: 289-300 - [c4]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert:
An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. Computer-Aided Diagnosis 2023 - [c3]Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho, Raissa Souza, Nils D. Forkert:
Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference. MIDL 2023: 1304-1317 - [i1]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert:
Towards objective and systematic evaluation of bias in medical imaging AI. CoRR abs/2311.02115 (2023) - 2022
- [j1]Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert:
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. J. Am. Medical Informatics Assoc. 30(1): 112-119 (2022) - 2021
- [c2]Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert:
Federated Learning Using Variable Local Training for Brain Tumor Segmentation. BrainLes@MICCAI (2) 2021: 392-404 - [c1]Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. BrainLes@MICCAI (2) 2021: 420-432
Coauthor Index
aka: Nils D. Forkert
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