b'Wenqi Li'

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

∙ 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

∙ 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

∙ 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

∙ 01/05/2021

Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation

Multi-modal medical image segmentation plays an essential role in clinic...

Jingkun Chen, 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

∙ 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

∙ 03/09/2020

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Knowledge graph models world knowledge as concepts, entities, and the re...

Xianpei Han, 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

Privacy-preserving Federated Brain Tumour Segmentation

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

Wenqi Li, et al. ∙

research

∙ 07/07/2019

Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Brain tissue segmentation from multimodal MRI is a key building block of...

Reuben Dorent, et al. ∙

research

∙ 06/10/2019

Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

Automatic segmentation of vestibular schwannoma (VS) tumors from magneti...

Guotai Wang, et al. ∙

research

∙ 02/18/2019

2017 Robotic Instrument Segmentation Challenge

In mainstream computer vision and machine learning, public datasets such...

Max Allan, et al. ∙

research

∙ 11/05/2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different d...

Spyridon Bakas, et al. ∙

research

∙ 10/18/2018

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis...

Guotai Wang, et al. ∙

research

∙ 08/22/2018

Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...

Kerstin Kläser, et al. ∙

research

∙ 07/19/2018

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Despite the state-of-the-art performance for medical image segmentation,...

Guotai Wang, et al. ∙

research

∙ 07/19/2018

Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation

Data augmentation has been widely used for training deep learning system...

Guotai Wang, et al. ∙

research

∙ 07/09/2018

Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration

One of the fundamental challenges in supervised learning for multimodal ...

Yipeng Hu, et al. ∙

research

∙ 06/18/2018

Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly...

Felix J. S. Bragman, et al. ∙

research

∙ 05/02/2018

Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability o...

Luis C. Garcia-Peraza-Herrera, et al. ∙

research

∙ 10/11/2017

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art perf...

Guotai Wang, et al. ∙

research

∙ 09/11/2017

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are i...

Eli Gibson, et al. ∙

research

∙ 07/11/2017

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Deep-learning has proved in recent years to be a powerful tool for image...

Carole H Sudre, et al. ∙

research

∙ 07/06/2017

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visua...

Wenqi Li, et al. ∙

research

∙ 07/03/2017

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis, surgical...

Guotai Wang, et al. ∙

research

∙ 07/03/2017

Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

The Dice score is widely used for binary segmentation due to its robustn...

Lucas Fidon, et al. ∙

research

∙ 06/25/2017

ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools

Real-time tool segmentation from endoscopic videos is an essential part ...

Luis C. Garcia-Peraza-Herrera, et al. ∙

research

∙ 06/25/2017

Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery....

Lucas Fidon, et al. ∙

Wenqi Li

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