b'Daguang Xu'

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 ...
03/30/2021 ∙ by Xiaosong Wang, et al. ∙ 0 ∙ share

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

Recently, neural architecture search (NAS) has been applied to automatic...
03/29/2021 ∙ by Yufan He, et al. ∙ 0 ∙ share

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Test-Time Training for Deformable Multi-Scale Image Registration

Registration is a fundamental task in medical robotics and is often a cr...
03/25/2021 ∙ by Wentao Zhu, et al. ∙ 1 ∙ share

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UNETR: Transformers for 3D Medical Image Segmentation

Fully Convolutional Neural Networks (FCNNs) with contracting and expansi...
03/18/2021 ∙ by Ali Hatamizadeh, et al. ∙ 0 ∙ share

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

Active learning is a unique abstraction of machine learning techniques w...
01/07/2021 ∙ by Vishwesh Nath, et al. ∙ 25 ∙ share

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

Previous work established skip-gram word2vec models could be used to min...
11/28/2020 ∙ by Leo K. Tam, et al. ∙ 0 ∙ share

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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...
11/23/2020 ∙ by Dong Yang, et al. ∙ 5 ∙ share

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Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

The performance of deep learning-based methods strongly relies on the nu...
09/28/2020 ∙ by Pochuan Wang, et al. ∙ 71 ∙ share

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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, ...
09/25/2020 ∙ by Vikash Gupta1, et al. ∙ 7 ∙ share

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Going to Extremes: Weakly Supervised Medical Image Segmentation

Medical image annotation is a major hurdle for developing precise and ro...
09/25/2020 ∙ by Holger R. Roth, et al. ∙ 39 ∙ share

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

Current deep learning paradigms largely benefit from the tremendous amou...
09/22/2020 ∙ by Xiaosong Wang, et al. ∙ 36 ∙ share

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Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies

CXRs are a crucial and extraordinarily common diagnostic tool, leading t...
09/11/2020 ∙ by Haomin Chen, et al. ∙ 0 ∙ share

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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...
07/31/2020 ∙ by Leo K. Tam, et al. ∙ 20 ∙ share

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Multi-Domain Image Completion for Random Missing Input Data

Multi-domain data are widely leveraged in vision applications taking adv...
07/10/2020 ∙ by Liyue Shen, et al. ∙ 0 ∙ share

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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...
06/28/2020 ∙ by Yingda Xia, et al. ∙ 3 ∙ share

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

Deep Learning (DL) models are becoming larger, because the increase in m...
06/22/2020 ∙ by Wentao Zhu, et al. ∙ 0 ∙ share

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

Deep neural network (DNN) based approaches have been widely investigated...
06/10/2020 ∙ by Dong Yang, et al. ∙ 2 ∙ share

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Enhancing Foreground Boundaries for Medical Image Segmentation

Object segmentation plays an important role in the modern medical image ...
05/29/2020 ∙ by Dong Yang, et al. ∙ 9 ∙ share

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When Radiology Report Generation Meets Knowledge Graph

Automatic radiology report generation has been an attracting research pr...
02/19/2020 ∙ by Yixiao Zhang, et al. ∙ 0 ∙ share

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VerSe: A Vertebrae Labelling and Segmentation Benchmark

In this paper we report the challenge set-up and results of the Large Sc...
01/24/2020 ∙ by Anjany Sekuboyina, et al. ∙ 0 ∙ share

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

3D convolution neural networks (CNN) have been proved very successful in...
12/20/2019 ∙ by Qihang Yu, et al. ∙ 46 ∙ share

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

Automatic segmentation of abdomen organs using medical imaging has many ...
10/15/2019 ∙ by Jinzheng Cai, et al. ∙ 0 ∙ share

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NeurReg: Neural Registration and Its Application to Image Segmentation

Registration is a fundamental task in medical image analysis which can b...
10/04/2019 ∙ by Wentao Zhu, et al. ∙ 20 ∙ share

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Cardiac Segmentation of LGE MRI with Noisy Labels

In this work, we attempt the segmentation of cardiac structures in late ...
10/02/2019 ∙ by Holger Roth, et al. ∙ 0 ∙ share

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Weakly supervised segmentation from extreme points

Annotation of medical images has been a major bottleneck for the develop...
10/02/2019 ∙ by Holger Roth, et al. ∙ 0 ∙ share

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Privacy-preserving Federated Brain Tumour Segmentation

Due to medical data privacy regulations, it is often infeasible to colle...
10/02/2019 ∙ by Wenqi Li, et al. ∙ 18 ∙ share

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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 ...
07/08/2019 ∙ by Ziyue Xu, et al. ∙ 0 ∙ share

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

Echocardiography has become routinely used in the diagnosis of cardiomyo...
06/18/2019 ∙ by Wentao Zhu, et al. ∙ 2 ∙ share

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4D CNN for semantic segmentation of cardiac volumetric sequences

We propose a 4D convolutional neural network (CNN) for the segmentation ...
06/17/2019 ∙ by Andriy Myronenko, et al. ∙ 0 ∙ share

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When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation

Recent advances in deep learning for medical image segmentation demonstr...
06/07/2019 ∙ by Ling Zhang, et al. ∙ 0 ∙ share

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

Deep learning algorithms, in particular 2D and 3D fully convolutional ne...
06/06/2019 ∙ by Zhuotun Zhu, et al. ∙ 1 ∙ share

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An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare ev...
03/26/2019 ∙ by Fengze Liu, et al. ∙ 0 ∙ share

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Interactive segmentation of medical images through fully convolutional neural networks

Image segmentation plays an essential role in medicine for both diagnost...
03/19/2019 ∙ by Tomas Sakinis, et al. ∙ 36 ∙ share

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

We propose a novel framework, uncertainty-aware multi-view co-training (...
11/29/2018 ∙ by Yingda Xia, et al. ∙ 0 ∙ share

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Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation

Simultaneous segmentation of multiple organs from different medical imag...
05/08/2018 ∙ by Saeid Asgari Taghanaki, et al. ∙ 2 ∙ share

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3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully ap...
11/23/2017 ∙ by Siqi Liu, et al. ∙ 0 ∙ share

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

Automatic liver segmentation in 3D medical images is essential in many c...
07/25/2017 ∙ by Dong Yang, et al. ∙ 0 ∙ share

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Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

Automatic localization and labeling of vertebra in 3D medical images pla...
05/17/2017 ∙ by Dong Yang, et al. ∙ 0 ∙ share

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Daguang Xu

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