540 citations · 1.2k across the 30 of their papers we have counts for
23 papers · 1 filter
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Jianghao Wu, Guotai Wang, Ran Gu +6
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…
BROW: Better featuRes fOr Whole slide image based on self-distillation
Yuanfeng Wu, Shaojie Li, Zhiqiang Du +1
Whole slide image (WSI) processing is becoming part of the key components of standard clinical diagnosis for various diseases. However, the direct application of conventional image…
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection
Wentao Zhu, Yufang Huang, Xiufeng Xie +5
The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram…
Anti-Retroactive Interference for Lifelong Learning
Runqi Wang, Yuxiang Bao, Baochang Zhang +3
Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive scie…
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
Hang Ye, Wentao Zhu, Chunyu Wang +2
While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for lar…
Adversarial Contrastive Self-Supervised Learning
Wentao Zhu, Hang Shang, Tingxun Lv +3
Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention. Self-supervised learning followed by the sup…
Associative Adversarial Learning Based on Selective Attack
Runqi Wang, Xiaoyue Duan, Baochang Zhang +4
A human's attention can intuitively adapt to corrupted areas of an image by recalling a similar uncorrupted image they have previously seen. This observation motivates us to improv…
MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks
Wentao Zhu, Zhuoqian Yang, Ziang Di +3
We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. In particular, our method is capable of retargeting b…
Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue
Shuwei Shao, Zhongcai Pei, Weihai Chen +4
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scena…
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices
Tianli Zhao, Xi Sheryl Zhang, Wentao Zhu +4
For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various…
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One
Shuwei Shao, Ran Li, Zhongcai Pei +5
Depth estimation attracts widespread attention in the computer vision community. However, it is still quite difficult to recover an accurate depth map using only one RGB image. We…
Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu +8
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning al…
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
Wentao Zhu, Yufang Huang, Mani A Vannan +5
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow…
Deep Learning for Automated Medical Image Analysis
Wentao Zhu
Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment…
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Wentao Zhu, Yufang Huang, Liang Zeng +6
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generati…
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Wentao Zhu, Yeeleng S. Vang, Yufang Huang +1
Recently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets label…
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
Wentao Zhu, Chaochun Liu, Wei Fan +1
In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule detection (identifying the l…
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Wentao Zhu, Xiang Xiang, Trac D. Tran +2
Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmen…
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Wentao Zhu, Chaochun Liu, Wei Fan +1
In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of…
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Wentao Zhu, Qi Lou, Yeeleng Scott Vang +1
Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to ann…
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Wentao Zhu, Qi Lou, Yeeleng Scott Vang +1
Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual…
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
Wentao Zhu, Xiang Xiang, Trac D. Tran +1
Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspir…
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Wentao Zhu, Cuiling Lan, Junliang Xing +4
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considerin…