163 citations · 224 across the 12 of their papers we have counts for
14 papers
Synergistic Network Learning and Label Correction for Noise-robust Image Classification
Chen Gong, Kong Bin, Eric J. Seibel +3
Large training datasets almost always contain examples with inaccurate or incorrect labels. Deep Neural Networks (DNNs) tend to overfit training label noise, resulting in poorer mo…
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Ziwei Luo, Jing Hu, Xin Wang +5
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In t…
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
Ziwei Luo, Jing Hu, Xin Wang +6
Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods ne…
Adaptive Warden Strategy for Countering Network Covert Storage Channels
Mehdi Chourib, Steffen Wendzel, Wojciech Mazurczyk
The detection and elimination of covert channels are performed by a network node, known as a warden. Especially if faced with adaptive covert communication parties, a regular warde…
Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +5
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a sur…
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
Quanyu Liao, Xin Wang, Bin Kong +4
The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses…
Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +4
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results. Their vulnerability has…
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
Xian Zhang, Xin Wang, Bin Kong +6
Prior knowledge of face shape and structure plays an important role in face inpainting. However, traditional face inpainting methods mainly focus on the generated image resolution…
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
Shanhui Sun, Jing Hu, Mingqing Yao +4
The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elabo…
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
Zhihui Guo, Junjie Bai, Yi Lu +5
A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simul…
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
Bin Kong, Xin Wang, Junjie Bai +7
Modeling the sequential information of image sequences has been a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb…
Residual Attention based Network for Hand Bone Age Assessment
Eric Wu, Bin Kong, Xin Wang +8
Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the…
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
Sarfaraz Hussein, Kunlin Cao, Qi Song +1
Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. Any improvement in robust and accurate nodule characterization can assist in identifyi…
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Sarfaraz Hussein, Robert Gillies, Kunlin Cao +2
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appe…