452 citations · 634 across the 16 of their papers we have counts for
16 papers
RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang +3
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of n…
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang +8
Harnessing the power of pre-training on large-scale datasets like ImageNet forms a fundamental building block for the progress of representation learning-driven solutions in comput…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Jingwei Sun, Ziyue Xu, Dong Yang +6
Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) dea…
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Meirui Jiang, Holger R Roth, Wenqi Li +6
How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness)…
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
Chun-Yin Huang, Ruinan Jin, Can Zhao +2
While Federated Learning (FL) is gaining popularity for training machine learning models in a decentralized fashion, numerous challenges persist, such as asynchronization, computat…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
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…
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…
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Yufan He, Dong Yang, Holger Roth +2
Recently, neural architecture search (NAS) has been applied to automatically search high-performance networks for medical image segmentation. The NAS search space usually contains…
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Wentao Zhu, Can Zhao, Wenqi Li +3
Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data paralle…
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN
Heran Yang, Jian Sun, Aaron Carass +4
The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guar…
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
Can Zhao, Aaron Carass, Blake E. Dewey +1
High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise.…