452 citations · 1.1k across the 29 of their papers we have counts for
30 papers
BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…
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…
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)…
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…
Learning Image Labels On-the-fly for Training Robust Classification Models
Xiaosong Wang, Ziyue Xu, Dong Yang +3
Current deep learning paradigms largely benefit from the tremendous amount of annotated data. However, the quality of the annotations often varies among labelers. Multi-observer st…
Going to Extremes: Weakly Supervised Medical Image Segmentation
Holger R Roth, Dong Yang, Ziyue Xu +2
Medical image annotation is a major hurdle for developing precise and robust machine learning models. Annotation is expensive, time-consuming, and often requires expert knowledge,…
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Bowen Chen, Huan Ling, Xiaohui Zeng +3
Manually labeling video datasets for segmentation tasks is extremely time consuming. In this paper, we introduce ScribbleBox, a novel interactive framework for annotating object in…
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
Dong Yang, Holger Roth, Ziyue Xu +3
Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the…
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…
Enhancing Foreground Boundaries for Medical Image Segmentation
Dong Yang, Holger Roth, Xiaosong Wang +3
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning. Given the various modaliti…
Capsules for Biomedical Image Segmentation
Rodney LaLonde, Ziyue Xu, Ismail Irmakci +2
Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. This is made possible via the introduction of locally-constrai…
When Radiology Report Generation Meets Knowledge Graph
Yixiao Zhang, Xiaosong Wang, Ziyue Xu +3
Automatic radiology report generation has been an attracting research problem towards computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning t…
NeurReg: Neural Registration and Its Application to Image Segmentation
Wentao Zhu, Andriy Myronenko, Ziyue Xu +5
Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignme…
Cardiac Segmentation of LGE MRI with Noisy Labels
Holger Roth, Wentao Zhu, Dong Yang +2
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step appro…
Weakly supervised segmentation from extreme points
Holger Roth, Ling Zhang, Dong Yang +4
Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically r…
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Ziyue Xu, Xiaosong Wang, Hoo-Chang Shin +5
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease. Conventionally, s…
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Ling Zhang, Xiaosong Wang, Dong Yang +7
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, in clinically realistic environments, such methods have marginal perform…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Interactive segmentation of medical images through fully convolutional neural networks
Tomas Sakinis, Fausto Milletari, Holger Roth +7
Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. M…
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
Dakai Jin, Ziyue Xu, Youbao Tang +2
Data availability plays a critical role for the performance of deep learning systems. This challenge is especially acute within the medical image domain, particularly when patholog…
White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections
Dakai Jin, Ziyue Xu, Adam P. Harrison +1
Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders. Although…