| Explainable classification of Parkinson’s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets M Camacho, M Wilms, P Mouches, H Almgren, R Souza, R Camicioli, ... NeuroImage: Clinical 38, 103405, 2023 | 33 | 2023 |
| Assessment of lipid and metabolite changes in obese calf muscle using multi-echo echo-planar correlated spectroscopic imaging R Nagarajan, CL Carpenter, CC Lee, N Michael, MK Sarma, R Souza, ... Scientific reports 7 (1), 17338, 2017 | 16 | 2017 |
| Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging EAM Stanley, R Souza, AJ Winder, V Gulve, K Amador, M Wilms, ... Journal of the American Medical Informatics Association 31 (11), 2613-2621, 2024 | 13 | 2024 |
| Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data R Souza, M Wilms, M Camacho, GB Pike, R Camicioli, O Monchi, ... Journal of the American Medical Informatics Association 30 (12), 1925-1933, 2023 | 11 | 2023 |
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science. R Souza, A Tuladhar, P Mouches, A Crimi, S Bakas Springer, Cham, 2022 | 11 | 2022 |
| An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction R Souza, P Mouches, M Wilms, A Tuladhar, S Langner, ND Forkert Journal of the American Medical Informatics Association 30 (1), 112-119, 2023 | 10 | 2023 |
| Identifying biases in a multicenter MRI database for Parkinson's disease classification: is the disease classifier a secret site classifier? R Souza, A Winder, EAM Stanley, V Vigneshwaran, M Camacho, ... IEEE Journal of Biomedical and Health Informatics 28 (4), 2047-2054, 2024 | 9 | 2024 |
| Pilot assessment of brain metabolism in perinatally HIV-infected youths using accelerated 5D echo planar J-resolved spectroscopic imaging Z Iqbal, NE Wilson, MA Keller, DE Michalik, JA Church, K Nielsen-Saines, ... PloS one 11 (9), e0162810, 2016 | 9 | 2016 |
| A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm R Souza, EAM Stanley, M Camacho, R Camicioli, O Monchi, Z Ismail, ... Frontiers in Artificial Intelligence 7, 1301997, 2024 | 7 | 2024 |
| A comparative analysis of the impact of data distribution on distributed learning with a traveling model for brain age prediction R Souza, A Aulakh, P Mouches, A Tuladhar, M Wilms, S Langner, ... Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and …, 2022 | 6 | 2022 |
| On the relationship between open science in artificial intelligence for medical imaging and global health equity R Souza, EAM Stanley, ND Forkert Workshop on Clinical Image-Based Procedures, 289-300, 2023 | 5 | 2023 |
| Multi-institutional travelling model for tumor segmentation in MRI datasets R Souza, A Tuladhar, P Mouches, M Wilms, L Tyagi, ND Forkert International MICCAI Brainlesion Workshop, 420-432, 2021 | 5 | 2021 |
| Federated learning using variable local training for brain tumor segmentation A Tuladhar, L Tyagi, R Souza, ND Forkert International MICCAI Brainlesion Workshop, 392-404, 2021 | 4 | 2021 |
| Foundation model-driven distributed learning for enhanced retinal age prediction C Nielsen, R Souza, M Wilms, ND Forkert Journal of the American Medical Informatics Association 31 (11), 2550-2559, 2024 | 2 | 2024 |
| Improved multi-site Parkinson’s disease classification using neuroimaging data with counterfactual inference V Vigneshwaran, M Wilms, MIC Camacho, R Souza, N Forkert Medical Imaging with Deep Learning, 1304-1317, 2024 | 2 | 2024 |
| An analysis of intensity harmonization techniques for Parkinson’s multi-site MRI datasets R Souza, EAM Stanley, M Camacho, M Wilms, ND Forkert Medical Imaging 2023: Computer-Aided Diagnosis 12465, 570-576, 2023 | 1 | 2023 |
| Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis R Souza, EAM Stanley, AJ Winder, C Kang, K Amador, EY Ohara, ... npj Digital Medicine 8 (1), 104, 2025 | | 2025 |
| Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data EAM Stanley, R Souza, M Wilms, ND Forkert EBioMedicine 111, 2025 | | 2025 |
| Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis EAM Stanley, R Souza, AJ Winder, M Wilms, GB Pike, G Dagasso, ... MICCAI Workshop on Fairness of AI in Medical Imaging, 163-175, 2024 | | 2024 |
| Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? R Souza, EAM Stanley, R Camicioli, O Monchi, Z Ismail, M Wilms, ... MICCAI Workshop on Fairness of AI in Medical Imaging, 119-128, 2024 | | 2024 |
