64 citations · 140 across the 18 of their papers we have counts for
22 papers
Multi-modal Imputation for Alzheimer's Disease Classification
Abhijith Shaji, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modaliti…
FedDAPL: Toward Client-Private Generalization in Federated Learning
Soroosh Safari Loaliyan, Jose-Luis Ambite, Paul M. Thompson +2
Federated Learning (FL) trains models locally at each research center or clinic and aggregates only model updates, making it a natural fit for medical imaging, where strict privacy…
Diffusion Bridge Models for 3D Medical Image Translation
Shaorong Zhang, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-w…
Task-Agnostic Federated Learning
Zhengtao Yao, Hong Nguyen, Ajitesh Srivastava +1
In the realm of medical imaging, leveraging large-scale datasets from various institutions is crucial for developing precise deep learning models, yet privacy concerns frequently i…
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows
Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral +2
A Federated Learning (FL) system typically consists of two core processing entities: the federation controller and the learners. The controller is responsible for managing the exec…
Federated Learning over Harmonized Data Silos
Dimitris Stripelis, Jose Luis Ambite
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sha…