624 citations · 879 across the 26 of their papers we have counts for
13 papers · 1 filter
Synthetic Augmentation with Large-scale Unconditional Pre-training
Jiarong Ye, Haomiao Ni, Peng Jin +2
Deep learning based medical image recognition systems often require a substantial amount of training data with expert annotations, which can be expensive and time-consuming to obta…
Securing Deep Generative Models with Universal Adversarial Signature
Yu Zeng, Mo Zhou, Yuan Xue +1
Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to…
Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis
Haomiao Ni, Yihao Liu, Sharon X. Huang +1
In this paper, we propose a novel dual-branch Transformation-Synthesis network (TS-Net), for video motion retargeting. Given one subject video and one driving video, TS-Net can pro…
HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation
Jian Jin, Yuan Xue, Xingxing Zhang +3
Significant improvement has been made on just noticeable difference (JND) modelling due to the development of deep neural networks, especially for the recently developed unsupervis…
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
Yuan Xue, Yuan-Chen Guo, Han Zhang +3
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have…
Synthetic Sample Selection via Reinforcement Learning
Jiarong Ye, Yuan Xue, L. Rodney Long +4
Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality c…
SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
Haomiao Ni, Yuan Xue, Qian Zhang +1
General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for the early detection of cerebral palsy (CP) in infants. Automated body parsing is a cru…
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
Yanglan Ou, Yuan Xue, Ye Yuan +4
Cervical cancer is the second most prevalent cancer affecting women today. As the early detection of cervical carcinoma relies heavily upon screening and pre-clinical testing, digi…
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
Yuan Xue, Hui Tang, Zhi Qiao +6
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall sha…
Selective Synthetic Augmentation with Quality Assurance
Yuan Xue, Jiarong Ye, Rodney Long +3
Supervised training of an automated medical image analysis system often requires a large amount of expert annotations that are hard to collect. Moreover, the proportions of data av…
Neural Wireframe Renderer: Learning Wireframe to Image Translations
Yuan Xue, Zihan Zhou, Xiaolei Huang
In architecture and computer-aided design, wireframes (i.e., line-based models) are widely used as basic 3D models for design evaluation and fast design iterations. However, unlike…
Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han +4
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate predi…
SegAN: Adversarial Network with Multi-scale Loss for Medical Image Segmentation
Yuan Xue, Tao Xu, Han Zhang +2
Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since…