b'Daguang Xu'

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

∙ 03/23/2022

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...

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...

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

∙ 02/14/2022

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

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

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...

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

∙ 10/06/2021

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

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

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...

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

∙ 03/25/2021

Test-Time Training for Deformable Multi-Scale Image Registration

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

Wentao Zhu, 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

∙ 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

∙ 11/28/2020

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

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

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...

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

∙ 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...

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...

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...

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...

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...

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...

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

∙ 02/19/2020

When Radiology Report Generation Meets Knowledge Graph

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

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...

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

∙ 10/04/2019

NeurReg: Neural Registration and Its Application to Image Segmentation

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

Wentao Zhu, 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

∙ 10/02/2019

Privacy-preserving Federated Brain Tumour Segmentation

Due to medical data privacy regulations, it is often infeasible to colle...

Wenqi Li, 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

∙ 06/18/2019

Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking

Echocardiography has become routinely used in the diagnosis of cardiomyo...

Wentao Zhu, 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

∙ 03/19/2019

Interactive segmentation of medical images through fully convolutional neural networks

Image segmentation plays an essential role in medicine for both diagnost...

Tomas Sakinis, 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. ∙

Daguang Xu

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