b'Daguang Xu'

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

∙ 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

∙ 03/04/2023

Federated Virtual Learning on Heterogeneous Data with Local-global Distillation

Despite Federated Learning (FL)'s trend for learning machine learning mo...

9 Chun-Yin Huang, et al. ∙

research

∙ 02/13/2023

PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees

Personalized Federated Learning (pFL) has emerged as a promising solutio...

1 Chulin Xie, 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

∙ 08/22/2022

Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation

Split learning (SL) has been proposed to train deep learning models in a...

0 Holger R. Roth, 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

∙ 03/23/2022

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

The lack of annotated datasets is a major challenge in training new task...

31 Andres Diaz-Pinto, et al. ∙

research

∙ 03/22/2022

GradViT: Gradient Inversion of Vision Transformers

In this work we demonstrate the vulnerability of vision transformers (Vi...

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

∙ 02/14/2022

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Federated learning (FL) allows the collaborative training of AI models w...

0 Ali Hatamizadeh, 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

∙ 10/06/2021

Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports

Localization and characterization of diseases like pneumonia are primary...

0 Riddhish Bhalodia, 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

∙ 03/25/2021

Test-Time Training for Deformable Multi-Scale Image Registration

Registration is a fundamental task in medical robotics and is often a cr...

1 Wentao Zhu, 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

∙ 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

∙ 11/28/2020

Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19

Previous work established skip-gram word2vec models could be used to min...

0 Leo K. Tam, 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

∙ 09/11/2020

Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies

CXRs are a crucial and extraordinarily common diagnostic tool, leading t...

0 Haomin Chen, et al. ∙

research

∙ 07/31/2020

Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies

Detecting clinically relevant objects in medical images is a challenge d...

20 Leo K. Tam, et al. ∙

research

∙ 07/10/2020

Multi-Domain Image Completion for Random Missing Input Data

Multi-domain data are widely leveraged in vision applications taking adv...

0 Liyue Shen, 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

∙ 06/22/2020

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in m...

0 Wentao Zhu, 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

∙ 02/19/2020

When Radiology Report Generation Meets Knowledge Graph

Automatic radiology report generation has been an attracting research pr...

0 Yixiao Zhang, 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

∙ 10/04/2019

NeurReg: Neural Registration and Its Application to Image Segmentation

Registration is a fundamental task in medical image analysis which can b...

20 Wentao Zhu, et al. ∙

Daguang Xu

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