b'Dong Yang'

research

∙ 09/21/2023

A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

In Time-Triggered (TT) or time-sensitive networks, the transmission of a...

0 Zonghui Li, et al. ∙

research

∙ 07/31/2023

Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training

Harnessing the power of pre-training on large-scale datasets like ImageN...

0 Jeya Maria Jose Valanarasu, et al. ∙

research

∙ 07/15/2023

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

We propose a novel neural deformable model (NDM) targeting at the recons...

0 Meng Ye, et al. ∙

research

∙ 06/06/2023

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models

Prompt engineering is an essential technique for enhancing the abilities...

0 Fobo Shi, et al. ∙

research

∙ 03/28/2023

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Federated learning is a popular collaborative learning approach that ena...

0 Jingwei Sun, et al. ∙

research

∙ 03/22/2023

Data-Driven Leader-following Consensus for Nonlinear Multi-Agent Systems against Composite Attacks: A Twins Layer Approach

This paper studies the leader-following consensuses of uncertain and non...

0 Xin Gong, et al. ∙

research

∙ 02/27/2023

Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech

Pause insertion, also known as phrase break prediction and phrasing, is ...

0 Dong Yang, et al. ∙

research

∙ 02/15/2023

Fast Blind Audio Copy-Move Detection and Localization Using Local Feature Tensors in Noise

The increasing availability of audio editing software altering digital a...

0 Dong Yang, et al. ∙

research

∙ 11/04/2022

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most a...

6 M. Jorge Cardoso, et al. ∙

research

∙ 10/24/2022

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables the building of robust and generalizable...

0 Holger R. Roth, et al. ∙

research

∙ 09/22/2022

Automated head and neck tumor segmentation from 3D PET/CT

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platf...

0 Andriy Myronenko, et al. ∙

research

∙ 09/21/2022

Automated segmentation of intracranial hemorrhages from 3D CT

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a ...

0 Md Mahfuzur Rahman Siddiquee, et al. ∙

research

∙ 09/20/2022

Automated ischemic stroke lesion segmentation from 3D MRI

Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a plat...

0 Md Mahfuzur Rahman Siddique, et al. ∙

research

∙ 09/13/2022

Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuning

Which volume to annotate next is a challenging problem in building medic...

0 Vishwesh Nath, et al. ∙

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 ...

0 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...

0 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...

0 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...

0 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...

0 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 ...

0 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...

0 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...

0 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...

0 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...

0 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...

21 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...

0 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...

0 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 ...

0 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...

0 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...

0 Dong Yang, et al. ∙

research

∙ 03/18/2021

UNETR: Transformers for 3D Medical Image Segmentation

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

0 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 ...

20 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...

3 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...

25 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...

5 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...

71 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, ...

7 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...

39 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...

36 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 ...

44 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...

16 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...

1 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...

3 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...

2 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 ...

9 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...

0 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...

0 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...

46 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 ...

0 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 ...

0 Holger Roth, et al. ∙

Dong Yang

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