Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants. - Nikita Nogovitsyn; Roberto Souza 001; Meghan Muller; Amelia Srajer; Stefanie Hassel; Stephen R. Arnott; Andrew D. Davis; Geoffrey B. C. Hall; Jacqueline K. Harris; Mojdeh Zamyadi; Paul D. Metzak; Zahinoor Ismail; et al. (NeuroImage, 2019)

Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling. - Anup Tuladhar; Sascha Gill; Zahinoor Ismail; Nils D. Forkert; Alzheimer's Disease Neuroimaging Initiative (J. Biomed. Informatics, 2020)

Modeling Neurodegeneration in silico With Deep Learning. - Anup Tuladhar; Jasmine A. Moore; Zahinoor Ismail; Nils D. Forkert (Frontiers Neuroinformatics, 2021)

Simulating progressive neurodegeneration in silico with deep artificial neural networks. - Anup Tuladhar; Jasmine A. Moore; Zahinoor Ismail; Nils D. Forkert (CogSci, 2022)

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. - Jasmine A. Moore; Anup Tuladhar; Zahinoor Ismail; Pauline Mouches; Matthias Wilms; Nils D. Forkert (Neuroinformatics, 2023)

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system. - Jasmine A. Moore; Matthias Wilms; Alejandro Gutierrez; Zahinoor Ismail; Kayson Fakhar; Fatemeh Hadaeghi; Claus C. Hilgetag; Nils D. Forkert (Frontiers Comput. Neurosci., 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)

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)

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)