Raissa Souza - RankMe

19 papers Journal 9Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accounting for population structure in deep learning models for genomic analysis. J. Biomed. Informatics Gabrielle Dagasso, Matthias Wilms, Raissa Souza, Nils D. Forkert
2025	—	`conf`	Assessing Annotator and Clinician Biases in an Open-Source-Based Tool Used to Generate Head CT Segmentations for Deep Learning Training. FAIMI@MICCAI Artur Jose Marques Paulo, Pedro Vinicius Alves Silva, Tayran Olegário, Paula Bresciani de Andrade, Klaus Schumacher, Rafael Maffei Loureiro, Joselisa Peres Queiroz de Paiva, Raissa Souza, Bruna Garbes Goncalves Pinto
2025	J	`jnl`	Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis. npj Digit. Medicine 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
2025	—	`conf`	Simulating Inter-observer Variability Across Clinical Experience Levels for Brain Tumour Segmentation. HAIC@MICCAI Haley Gillett, Emma A. M. Stanley, Raissa Souza, Matthias Wilms, Nils D. Forkert
2024	J	`jnl`	A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. Frontiers Artif. Intell. Raissa Souza, Emma A. M. Stanley, Milton Camacho, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert
2024	—	`conf`	Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. FAIMI/EPIMI@MICCAI Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Matthias Wilms, G. Bruce Pike, Gabrielle Dagasso, Christopher Nielsen, Sarah J. MacEachern, Nils D. Forkert
2024	—	`—`	Distributed Deep Learning Methods for Medical Imaging Analysis. Raissa Souza
2024	—	`conf`	Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? FAIMI/EPIMI@MICCAI Raissa Souza, Emma A. M. Stanley, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert
2024	J	`jnl`	Foundation model-driven distributed learning for enhanced retinal age prediction. J. Am. Medical Informatics Assoc. Christopher Nielsen, Raissa Souza, Matthias Wilms, Nils D. Forkert
2024	J	`jnl`	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 Raissa Souza, Anthony J. Winder, Emma A. M. Stanley, Vibujithan Vigneshwaran, Milton Camacho, Richard Camicioli, Oury Monchi, Matthias Wilms, Nils D. Forkert
2024	J	`jnl`	Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. J. Am. Medical Informatics Assoc. Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert
2023	—	`conf`	An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. Computer-Aided Diagnosis Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert
2023	J	`jnl`	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. Raissa Souza, Matthias Wilms, Milton Camacho, G. Bruce Pike, Richard Camicioli, Oury Monchi, Nils D. Forkert
2023	—	`conf`	Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference. MIDL Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho, Raissa Souza, Nils D. Forkert
2023	—	`conf`	On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. CLIP/FAIMI/EPIMI@MICCAI Raissa Souza, Emma A. M. Stanley, Nils D. Forkert
2023	J	`jnl`	Towards objective and systematic evaluation of bias in medical imaging AI. CoRR Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert
2022	J	`jnl`	An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. J. Am. Medical Informatics Assoc. Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert
2021	—	`conf`	Federated Learning Using Variable Local Training for Brain Tumor Segmentation. BrainLes@MICCAI (2) Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert
2021	—	`conf`	Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. BrainLes@MICCAI (2) Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert