Search Results for author: Ziyue Xu

Found 37 papers, 6 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

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

no code implementations CVPR 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

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.

FedGL: Federated Graph Learning Framework with Global Self-Supervision

no code implementations7 May 2021 Chuan Chen, Weibo Hu, Ziyue Xu, Zibin Zheng

Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients.

Federated Learning Graph Learning +1

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

no code implementations7 Apr 2021 Ugur Demir, Ismail Irmakci, Elif Keles, Ahmet Topcu, Ziyue Xu, Concetto Spampinato, Sachin Jambawalikar, Evrim Turkbey, Baris Turkbey, Ulas Bagci

We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.

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

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

ScribbleBox: Interactive Annotation Framework for Video Object Segmentation

no code implementations ECCV 2020 Bo-Wen Chen, Huan Ling, Xiaohui Zeng, Gao Jun, Ziyue Xu, Sanja Fidler

Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.

Semantic Segmentation Video Object Segmentation +1

GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

no code implementations10 Aug 2020 Hoo-chang Shin, Alvin Ihsani, Ziyue Xu, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, Alzheimer's Disease Neuroimaging Initiative

This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.

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

Capsules for Biomedical Image Segmentation

no code implementations9 Apr 2020 Rodney LaLonde, Ziyue Xu, Ismail Irmakci, Sanjay Jain, Ulas Bagci

The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.

Computed Tomography (CT) Semantic Segmentation

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

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.)

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

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

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

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

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

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

White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections

no code implementations19 Mar 2018 Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura

Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders.

Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

no code implementations12 Jun 2017 Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.

Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

no code implementations19 Jan 2017 Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura

Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.

Computed Tomography (CT) Multi-Label Learning +1

CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans

no code implementations11 Jul 2014 Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Deborah Douglas, Jeffrey M. Solomon, Jayaram K. Udupa, Daniel J. Mollura

Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.

Computed Tomography (CT) Semantic Segmentation

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