b'Ziyue 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

∙ 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

∙ 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

∙ 03/18/2022

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

∙ 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

∙ 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

∙ 05/07/2021

FedGL: Federated Graph Learning Framework with Global Self-Supervision

Graph data are ubiquitous in the real world. Graph learning (GL) tries t...

0 Chuan Chen, et al. ∙

research

∙ 04/07/2021

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

Visual explanation methods have an important role in the prognosis of th...

13 Ugur Demir, 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

∙ 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

∙ 09/25/2020

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

∙ 08/22/2020

ScribbleBox: Interactive Annotation Framework for Video Object Segmentation

Manually labeling video datasets for segmentation tasks is extremely tim...

0 Bowen Chen, et al. ∙

research

∙ 08/10/2020

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

Positron Emission Tomography (PET) is now regarded as the gold standard ...

0 Hoo-chang Shin, 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

∙ 04/09/2020

Capsules for Biomedical Image Segmentation

Our work expands the use of capsule networks to the task of object segme...

13 Rodney LaLonde, 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

∙ 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. ∙

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. ∙

research

∙ 10/02/2019

Weakly supervised segmentation from extreme points

Annotation of medical images has been a major bottleneck for the develop...

0 Holger Roth, 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 ...

0 Ziyue Xu, 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...

0 Ling Zhang, et al. ∙

research

∙ 04/01/2019

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presum...

0 Hugo J. Kuijf, 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...

36 Tomas Sakinis, et al. ∙

research

∙ 06/11/2018

CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

Data availability plays a critical role for the performance of deep lear...

2 Dakai Jin, et al. ∙

research

∙ 03/19/2018

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

Segmentation and quantification of white matter hyperintensities (WMHs) ...

0 Dakai Jin, et al. ∙

research

∙ 06/12/2017

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

Pathological lung segmentation (PLS) is an important, yet challenging, m...

0 Adam P. Harrison, et al. ∙

research

∙ 01/19/2017

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

Accurately predicting and detecting interstitial lung disease (ILD) patt...

0 Mingchen Gao, et al. ∙

research

∙ 09/21/2016

Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features

Computed tomography imaging is a standard modality for detecting and ass...

0 Mario Buty, et al. ∙

research

∙ 02/10/2016

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Remarkable progress has been made in image recognition, primarily due to...

0 Hoo-chang Shin, et al. ∙

research

∙ 07/11/2014

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

Accurate and fast extraction of lung volumes from computed tomography (C...

0 Awais Mansoor, et al. ∙

Ziyue Xu

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