Search Results for author: Daguang Xu

Found 62 papers, 16 papers with code

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

1 code implementation1 Apr 2022 Ali Hatamizadeh, Ziyue Xu, Dong Yang, Wenqi Li, Holger Roth, Daguang Xu

Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to dataset and model sizes which has led to state-of-the-art performance in various computer vision and medical image analysis tasks.

Brain Tumor Segmentation Representation Learning +1

GradViT: Gradient Inversion of Vision Transformers

no code implementations22 Mar 2022 Ali Hatamizadeh, Hongxu Yin, Holger Roth, Wenqi Li, Jan Kautz, Daguang Xu, Pavlo Molchanov

In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks.

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

no code implementations18 Mar 2022 An Xu, Wenqi Li, Pengfei Guo, Dong Yang, Holger Roth, Ali Hatamizadeh, Can Zhao, Daguang Xu, Heng Huang, Ziyue Xu

In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time.

Federated Learning Medical Image Segmentation +1

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

1 code implementation4 Jan 2022 Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.

3D Semantic Segmentation Brain Tumor Segmentation

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

no code implementations20 Dec 2021 Cheng Peng, Andriy Myronenko, Ali Hatamizadeh, Vish Nath, Md Mahfuzur Rahman Siddiquee, Yufan He, Daguang Xu, Rama Chellappa, Dong Yang

Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures.

Medical Image Segmentation Neural Architecture Search +1

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

1 code implementation29 Nov 2021 Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ali Hatamizadeh

Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.

Computed Tomography (CT) Medical Image Segmentation +1

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

no code implementations6 Oct 2021 Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam, Ziyue Xu, Xiaosong Wang, Evrim Turkbey, Daguang Xu

Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

no code implementations19 Aug 2021 Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.

Federated Learning Pancreas Segmentation +1

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

no code implementations12 Jul 2021 Vishwesh Nath, Dong Yang, Ali Hatamizadeh, Anas A. Abidin, Andriy Myronenko, Holger Roth, Daguang Xu

First, we show higher correlation to using full data for training when testing on the external validation set using smaller proxy data than a random selection of the proxy data.

AutoML Medical Image Segmentation +1

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

no code implementations20 Apr 2021 Yingda Xia, Dong Yang, Wenqi Li, Andriy Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, Peng An, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Anna Ierardi, Alan Yuille, Holger Roth

In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.

Federated Learning Lesion Segmentation +2

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

no code implementations30 Mar 2021 Xiaosong Wang, Ziyue Xu, Leo Tam, Dong Yang, Daguang Xu

In this work, we introduce an image-text pre-training framework that can learn from these raw data with mixed data inputs, i. e., paired image-text data, a mixture of paired and unpaired data.

Language Modelling Masked Language Modeling

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

1 code implementation CVPR 2021 Yufan He, Dong Yang, Holger Roth, Can Zhao, Daguang Xu

In this work, we focus on three important aspects of NAS in 3D medical image segmentation: flexible multi-path network topology, high search efficiency, and budgeted GPU memory usage.

Medical Image Segmentation Neural Architecture Search +1

Test-Time Training for Deformable Multi-Scale Image Registration

no code implementations25 Mar 2021 Wentao Zhu, Yufang Huang, Daguang Xu, Zhen Qian, Wei Fan, Xiaohui Xie

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.

Image Registration Semantic Segmentation

UNETR: Transformers for 3D Medical Image Segmentation

7 code implementations18 Mar 2021 Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, Daguang Xu

Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.

3D Medical Imaging Segmentation Semantic Segmentation

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

no code implementations7 Jan 2021 Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth

The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.

Active Learning Hippocampus +2

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

no code implementations28 Nov 2020 Leo K. Tam, Xiaosong Wang, Daguang Xu

To stimulate COVID-19 research, we release an influenza clinical trials and antiviral analogies dataset used in conjunction with the COVID-19 Open Research Dataset Challenge (CORD-19) literature dataset in the study.

Drug Discovery Language Modelling

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

no code implementations25 Sep 2020 Vikash Gupta1, Holger Roth, Varun Buch3, Marcio A. B. C. Rockenbach, Richard D. White, Dong Yang, Olga Laur, Brian Ghoshhajra, Ittai Dayan, Daguang Xu, Mona G. Flores, Barbaros Selnur Erdal

The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology.

Transfer Learning

Going to Extremes: Weakly Supervised Medical Image Segmentation

2 code implementations25 Sep 2020 Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu

Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.

Medical Image Segmentation Semantic Segmentation

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

no code implementations22 Sep 2020 Xiaosong Wang, Ziyue Xu, Dong Yang, Leo Tam, Holger Roth, Daguang Xu

We apply the attention-on-label scheme on the classification task of a synthetic noisy CIFAR-10 dataset to prove the concept, and then demonstrate superior results (3-5% increase on average in multiple disease classification AUCs) on the chest x-ray images from a hospital-scale dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular training paradigms.

General Classification Robust classification

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

1 code implementation31 Jul 2020 Leo K. Tam, Xiaosong Wang, Evrim Turkbey, Kevin Lu, Yuhong Wen, Daguang Xu

The architectural modifications address three obstacles -- implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities.

Head Detection Referring Expression

Multi-Domain Image Completion for Random Missing Input Data

no code implementations10 Jul 2020 Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie Anne Harmon, Thomas Hogue Sanford, Sherif Mehralivand, Peter Choyke, Bradford Wood, Daguang Xu

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).

Brain Tumor Segmentation Disentanglement +1

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

no code implementations28 Jun 2020 Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.

Pancreas Segmentation Semantic Segmentation +3

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

1 code implementation22 Jun 2020 Wentao Zhu, Can Zhao, Wenqi Li, Holger Roth, Ziyue Xu, Daguang Xu

In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy.

Semantic Segmentation

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

no code implementations10 Jun 2020 Dong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu

For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.

Data Augmentation Medical Image Segmentation +2

Enhancing Foreground Boundaries for Medical Image Segmentation

no code implementations MIDL 2019 Dong Yang, Holger Roth, Xiaosong Wang, Ziyue Xu, Andriy Myronenko, Daguang Xu

Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning.

Medical Image Segmentation Semantic Segmentation

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.

Federated Learning

When Radiology Report Generation Meets Knowledge Graph

no code implementations19 Feb 2020 Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, Daguang Xu

In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph.

Graph Embedding Image Captioning

Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network

no code implementations MIDL 2019 Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu

In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.

Image Generation

Training Models 20X Faster in Medical Image Analysis

no code implementations MIDL 2019 Dong Yang, Holger Roth, Xiaosong Wang, Ziyue Xu, Yan Cheng, Daguang Xu

Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.)

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

no code implementations CVPR 2020 Qihang Yu, Dong Yang, Holger Roth, Yutong Bai, Yixiao Zhang, Alan L. Yuille, Daguang Xu

3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.

Medical Image Segmentation Neural Architecture Search +1

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

no code implementations15 Oct 2019 Jinzheng Cai, Yingda Xia, Dong Yang, Daguang Xu, Lin Yang, Holger Roth

However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs.

Pancreas Segmentation

NeurReg: Neural Registration and Its Application to Image Segmentation

1 code implementation4 Oct 2019 Wentao Zhu, Andriy Myronenko, Ziyue Xu, Wenqi Li, Holger Roth, Yufang Huang, Fausto Milletari, Daguang Xu

Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.

Image Registration Multi-Task Learning +1

Cardiac Segmentation of LGE MRI with Noisy Labels

no code implementations2 Oct 2019 Holger Roth, Wentao Zhu, Dong Yang, Ziyue Xu, Daguang Xu

In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation.

Cardiac Segmentation Data Augmentation +1

Weakly supervised segmentation from extreme points

no code implementations2 Oct 2019 Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu

Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.

Weakly supervised segmentation

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

no code implementations8 Jul 2019 Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu

Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.

Image Generation

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

no code implementations18 Jun 2019 Wentao Zhu, Yufang Huang, Mani A. Vannan, Shizhen Liu, Daguang Xu, Wei Fan, Zhen Qian, Xiaohui Xie

In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.

4D CNN for semantic segmentation of cardiac volumetric sequences

no code implementations17 Jun 2019 Andriy Myronenko, Dong Yang, Varun Buch, Daguang Xu, Alvin Ihsani, Sean Doyle, Mark Michalski, Neil Tenenholtz, Holger Roth

We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time.

Semantic Segmentation

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

no code implementations6 Jun 2019 Zhuotun Zhu, Chenxi Liu, Dong Yang, Alan Yuille, Daguang Xu

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation.

Neural Architecture Search Semantic Segmentation +1

An Alarm System For Segmentation Algorithm Based On Shape Model

no code implementations ICLR 2019 Fengze Liu, Yingda Xia, Dong Yang, Alan Yuille, Daguang Xu

Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data. The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks.

Interactive segmentation of medical images through fully convolutional neural networks

no code implementations19 Mar 2019 Tomas Sakinis, Fausto Milletari, Holger Roth, Panagiotis Korfiatis, Petro Kostandy, Kenneth Philbrick, Zeynettin Akkus, Ziyue Xu, Daguang Xu, Bradley J. Erickson

Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.

Computed Tomography (CT) Interactive Segmentation +1

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

no code implementations29 Nov 2018 Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.

Medical Image Segmentation Tumor Segmentation

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

no code implementations25 Jul 2017 Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.

Liver Segmentation

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