b'Dong Yang'

research

∙ 07/23/2022

Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering

The Kalman filter has been adopted in acoustic echo cancellation due to ...

Dong Yang, et al. ∙

research

∙ 04/01/2022

UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

Vision Transformers (ViT)s have recently become popular due to their out...

Ali Hatamizadeh, et al. ∙

research

∙ 03/18/2022

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

Cross-silo federated learning (FL) has attracted much attention in medic...

An Xu, et al. ∙

research

∙ 03/12/2022

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that...

Pengfei Guo, et al. ∙

research

∙ 01/04/2022

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Semantic segmentation of brain tumors is a fundamental medical image ana...

Ali Hatamizadeh, et al. ∙

research

∙ 12/20/2021

HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet

Semantic segmentation of 3D medical images is a challenging task due to ...

Cheng Peng, et al. ∙

research

∙ 11/29/2021

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Vision Transformers (ViT)s have shown great performance in self-supervis...

Yucheng Tang, et al. ∙

research

∙ 11/15/2021

T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging

Lesion segmentation in medical imaging has been an important topic in cl...

Dong Yang, et al. ∙

research

∙ 11/01/2021

Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging

Multiple instance learning (MIL) is a key algorithm for classification o...

Andriy Myronenko, et al. ∙

research

∙ 08/19/2021

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Federated learning (FL) for medical image segmentation becomes more chal...

Chen Shen, et al. ∙

research

∙ 07/16/2021

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Building robust deep learning-based models requires diverse training dat...

Holger R. Roth, et al. ∙

research

∙ 07/12/2021

The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation

Deep learning models for medical image segmentation are primarily data-d...

Vishwesh Nath, et al. ∙

research

∙ 04/20/2021

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

Federated learning (FL) enables collaborative model training while prese...

Yingda Xia, et al. ∙

research

∙ 03/30/2021

Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays

Pre-trained models, e.g., from ImageNet, have proven to be effective in ...

Xiaosong Wang, et al. ∙

research

∙ 03/29/2021

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

Recently, neural architecture search (NAS) has been applied to automatic...

Yufan He, et al. ∙

research

∙ 03/19/2021

Biomedical Convergence Facilitated by the Emergence of Technological and Informatic Capabilities

We analyzed Medical Subject Headings (MeSH) from 21.6 million research a...

Dong Yang, et al. ∙

research

∙ 03/18/2021

UNETR: Transformers for 3D Medical Image Segmentation

Fully Convolutional Neural Networks (FCNNs) with contracting and expansi...

Ali Hatamizadeh, et al. ∙

research

∙ 03/04/2021

DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images

Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard ...

Meng Ye, et al. ∙

research

∙ 02/07/2021

SR-Affine: High-quality 3D hand model reconstruction from UV Maps

Under various poses and heavy occlusions,3D hand model reconstruction ba...

Ping Chen, et al. ∙

research

∙ 01/07/2021

Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation

Active learning is a unique abstraction of machine learning techniques w...

Vishwesh Nath, et al. ∙

research

∙ 11/23/2020

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

The recent outbreak of COVID-19 has led to urgent needs for reliable dia...

Dong Yang, et al. ∙

research

∙ 09/28/2020

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

The performance of deep learning-based methods strongly relies on the nu...

Pochuan Wang, et al. ∙

research

∙ 09/25/2020

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

The training of deep learning models typically requires extensive data, ...

Vikash Gupta1, et al. ∙

research

∙ 09/25/2020

Going to Extremes: Weakly Supervised Medical Image Segmentation

Medical image annotation is a major hurdle for developing precise and ro...

Holger R. Roth, et al. ∙

research

∙ 09/22/2020

Learning Image Labels On-the-fly for Training Robust Classification Models

Current deep learning paradigms largely benefit from the tremendous amou...

Xiaosong Wang, et al. ∙

research

∙ 08/19/2020

Enhanced MRI Reconstruction Network using Neural Architecture Search

The accurate reconstruction of under-sampled magnetic resonance imaging ...

Qiaoying Huang, et al. ∙

research

∙ 08/18/2020

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (M...

Meng Ye, et al. ∙

research

∙ 07/22/2020

Time-aware Graph Embedding: A temporal smoothness and task-oriented approach

Knowledge graph embedding, which aims to learn the low-dimensional repre...

Yonghui Xu, et al. ∙

research

∙ 06/28/2020

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

Although having achieved great success in medical image segmentation, de...

Yingda Xia, et al. ∙

research

∙ 06/10/2020

Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

Deep neural network (DNN) based approaches have been widely investigated...

Dong Yang, et al. ∙

research

∙ 05/29/2020

Enhancing Foreground Boundaries for Medical Image Segmentation

Object segmentation plays an important role in the modern medical image ...

Dong Yang, et al. ∙

research

∙ 03/06/2020

Centrality Graph Convolutional Networks for Skeleton-based Action Recognition

The topological structure of skeleton data plays a significant role in h...

Dong Yang, et al. ∙

research

∙ 01/24/2020

VerSe: A Vertebrae Labelling and Segmentation Benchmark

In this paper we report the challenge set-up and results of the Large Sc...

Anjany Sekuboyina, et al. ∙

research

∙ 12/20/2019

C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation

3D convolution neural networks (CNN) have been proved very successful in...

Qihang Yu, et al. ∙

research

∙ 10/15/2019

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Automatic segmentation of abdomen organs using medical imaging has many ...

Jinzheng Cai, et al. ∙

research

∙ 10/02/2019

Cardiac Segmentation of LGE MRI with Noisy Labels

In this work, we attempt the segmentation of cardiac structures in late ...

Holger Roth, et al. ∙

research

∙ 10/02/2019

Weakly supervised segmentation from extreme points

Annotation of medical images has been a major bottleneck for the develop...

Holger Roth, et al. ∙

research

∙ 07/08/2019

Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network

Radiogenomic map linking image features and gene expression profiles is ...

Ziyue Xu, et al. ∙

research

∙ 06/17/2019

4D CNN for semantic segmentation of cardiac volumetric sequences

We propose a 4D convolutional neural network (CNN) for the segmentation ...

Andriy Myronenko, et al. ∙

research

∙ 06/07/2019

When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation

Recent advances in deep learning for medical image segmentation demonstr...

Ling Zhang, et al. ∙

research

∙ 06/06/2019

V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation

Deep learning algorithms, in particular 2D and 3D fully convolutional ne...

Zhuotun Zhu, et al. ∙

research

∙ 03/26/2019

An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare ev...

Fengze Liu, et al. ∙

research

∙ 11/29/2018

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

We propose a novel framework, uncertainty-aware multi-view co-training (...

Yingda Xia, et al. ∙

research

∙ 10/18/2018

MRI Reconstruction via Cascaded Channel-wise Attention Network

We consider an MRI reconstruction problem with input of k-space data at ...

Qiaoying Huang, et al. ∙

research

∙ 10/12/2018

Important Attribute Identification in Knowledge Graph

The knowledge graph(KG) composed of entities with their descriptions and...

Shengjie Sun, et al. ∙

research

∙ 05/23/2018

Saliency deep embedding for aurora image search

Deep neural networks have achieved remarkable success in the field of im...

Xi Yang, et al. ∙

research

∙ 07/25/2017

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Automatic liver segmentation in 3D medical images is essential in many c...

Dong Yang, et al. ∙

research

∙ 05/17/2017

Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

Automatic localization and labeling of vertebra in 3D medical images pla...

Dong Yang, et al. ∙

research

∙ 12/28/2013

Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition

We present a novel Discriminant Locality Preserving Projections (DLPP) a...

Sheng Huang, et al. ∙

Dong Yang

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