452 citations · 1k across the 17 of their papers we have counts for
9 papers · 1 filter
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Holger R. Roth, Yan Cheng, Yuhong Wen +20
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVI…
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Holger R. Roth, Ali Hatamizadeh, Ziyue Xu +4
Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be…
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation
Ali Hatamizadeh, Ziyue Xu, Dong Yang +3
Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to datas…
GradViT: Gradient Inversion of Vision Transformers
Ali Hatamizadeh, Hongxu Yin, Holger Roth +4
In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given…
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath +14
The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive an…
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
An Xu, Wenqi Li, Pengfei Guo +7
Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years as it can resolve the critical issues of insufficient…
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Pengfei Guo, Dong Yang, Ali Hatamizadeh +14
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving…
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Ali Hatamizadeh, Hongxu Yin, Pavlo Molchanov +8
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare application…