Search Results for author: Qian Wang

Found 79 papers, 23 papers with code

Reconstructing Training Data from Diverse ML Models by Ensemble Inversion

no code implementations5 Nov 2021 Qian Wang, Daniel Kurz

This technique leads to noticeable improvements of the quality of the generated samples with distinguishable features of the dataset entities compared to MI of a single ML model.

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

no code implementations25 Oct 2021 Qian Wang, Fanlin Meng, Toby P. Breckon

The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes.

Domain Adaptation Image Classification

Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information

no code implementations19 Oct 2021 Baolin Zheng, Peipei Jiang, Qian Wang, Qi Li, Chao Shen, Cong Wang, Yunjie Ge, Qingyang Teng, Shenyi Zhang

For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary.

Adversarial Attack Global Optimization +1

Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images

2 code implementations30 Sep 2021 Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim

In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.

Motion Estimation Unsupervised Landmark Detection

Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties

no code implementations29 Sep 2021 Minglei You, Qian Wang, Hongjian Sun, Ivan Castro, Jing Jiang

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction.

Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network

1 code implementation12 Aug 2021 Kai Xuan, Lei Xiang, Xiaoqian Huang, Lichi Zhang, Shu Liao, Dinggang Shen, Qian Wang

First, the spatial alignment network estimates the spatial misalignment between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly.

MRI Reconstruction

FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning

no code implementations8 Aug 2021 Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.

Federated Learning Non-Intrusive Load Monitoring

Electrical peak demand forecasting- A review

no code implementations3 Aug 2021 Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen

To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature.

Intrusion Detection and Localization for Networked Embedded Control Systems

no code implementations17 Jun 2021 Vuk Lesi, Marcio Juliato, Shabbir Ahmed, Christopher Gutierrez, Qian Wang, Manoj Sastry

In this paper we present a physics-based Intrusion Detection System (IDS) aimed at increasing the security in control systems.

Intrusion Detection

CARTL: Cooperative Adversarially-Robust Transfer Learning

1 code implementation12 Jun 2021 Dian Chen, Hongxin Hu, Qian Wang, Yinli Li, Cong Wang, Chao Shen, Qi Li

In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.

Adversarial Robustness Fine-tuning +1

FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework

no code implementations25 Apr 2021 Chen Zhao, Zhipeng Gao, Qian Wang, Kaile Xiao, Zijia Mo, M. Jamal Deen

With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving.

Federated Learning

How to distribute data across tasks for meta-learning?

no code implementations15 Mar 2021 Alexandru Cioba, Michael Bromberg, Qian Wang, Ritwik Niyogi, Georgios Batzolis, Jezabel Garcia, Da-Shan Shiu, Alberto Bernacchia

We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique and constant optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks.

Few-Shot Image Classification Meta-Learning

A bottom-up quantification of flexibility potential from the thermal energy storage in electric space heating

no code implementations11 Mar 2021 Lars Herre, Behrouz Nourozi, Mohammad Reza Hesamzadeh, Qian Wang, Lennart Söder

To this end, dwellings with heat pumps and direct electric heaters are modeled as thermal energy storage equivalents that can be included in a linear two-stage problem formulation.

Modularity and Mutual Information in Networks: Two Sides of the Same Coin

no code implementations3 Mar 2021 Yongkang Guo, Zhihuan Huang, Yuqing Kong, Qian Wang

At a high level, we show the significance of community structure is equivalent to the amount of information contained in the network.

Community Detection Social and Information Networks

DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques

no code implementations24 Feb 2021 Md Mamunur Rahaman, Chen Li, YuDong Yao, Frank Kulwa, Xiangchen Wu, Xiaoyan Li, Qian Wang

Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors.

Cell Segmentation Classification +1

Recent Advances in Adversarial Training for Adversarial Robustness

no code implementations2 Feb 2021 Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang

Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.

Adversarial Robustness

Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery

no code implementations21 Dec 2020 Qian Wang, Toby P. Breckon

Specifically, we formulate it as a 3D semantic segmentation problem to identify material types for all voxels based on which contraband materials can be detected.

3D Semantic Segmentation Computed Tomography (CT) +1

Data Augmentation with norm-VAE for Unsupervised Domain Adaptation

1 code implementation1 Dec 2020 Qian Wang, Fanlin Meng, Toby P. Breckon

As a result, our proposed methods (i. e. naive-SPL and norm-VAE-SPL) can achieve new state-of-the-art performance with the average accuracy of 93. 4% and 90. 4% on Office-Caltech and ImageCLEF-DA datasets, and comparable performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97. 2%, 87. 6% and 67. 9% respectively.

Data Augmentation Image Classification +1

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

no code implementations29 Sep 2020 Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang

In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.

Collaborative Fairness in Federated Learning

1 code implementation27 Aug 2020 Lingjuan Lyu, Xinyi Xu, Qian Wang

In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.

Fairness Federated Learning

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery

no code implementations3 Aug 2020 Qian Wang, Neelanjan Bhowmik, Toby P. Breckon

X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibited item detection focus primarily on 2D X-ray imagery.

3D Object Detection Computed Tomography (CT) +1

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders

no code implementations3 Aug 2020 Qian Wang, Toby P. Breckon

In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source domain samples as semantic representations for zero-shot learning.

Domain Adaptation Generalized Zero-Shot Learning

Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint

no code implementations6 Jul 2020 Yifan Gu, Qian Wang, He Chen, Yonghui Li, Branka Vucetic

We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC).

Information Theory Networking and Internet Architecture Signal Processing Information Theory

mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography

no code implementations25 May 2020 Sheng Wang, Jiayu Huo, Xi Ouyang, Jifei Che, Xuhua Ren, Zhong Xue, Qian Wang, Jie-Zhi Cheng

However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.

Style Transfer

A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency

no code implementations19 May 2020 Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang

With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.

Classification General Classification +1

Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation

1 code implementation26 Apr 2020 Qian Wang, Toby P. Breckon

Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e. g., texts and images) or feature dimensions (e. g., features extracted with different methods).

Domain Adaptation Transfer Learning

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth

1 code implementation27 Mar 2020 Kai Xuan, Liping Si, Lichi Zhang, Zhong Xue, Yining Jiao, Weiwu Yao, Dinggang Shen, Dijia Wu, Qian Wang

In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images with small slice spacing from low-resolution (LR) inputs of large slice spacing.


On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery

no code implementations27 Mar 2020 Qian Wang, Neelanjan Bhowmik, Toby P. Breckon

As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features.

3D Object Detection Computed Tomography (CT) +1

Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

4 code implementations12 Mar 2020 Ke Wu, Didier Darcet, Qian Wang, Didier Sornette

Japan and Italy are in serious situations with no short-term end to the outbreak to be expected.

Populations and Evolution Biological Physics Applications

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

1 code implementation CVPR 2020 Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim

SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.

Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions

no code implementations22 Feb 2020 Minghui Li, Sherman S. M. Chow, Shengshan Hu, Yuejing Yan, Chao Shen, Qian Wang

This paper proposes a new scheme for privacy-preserving neural network prediction in the outsourced setting, i. e., the server cannot learn the query, (intermediate) results, and the model.

A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes

1 code implementation15 Jan 2020 Qian Wang, Najla Megherbi, Toby P. Breckon

Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner.

Computed Tomography (CT)

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

1 code implementation18 Nov 2019 Qian Wang, Toby P. Breckon

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains.

Structured Prediction Unsupervised Domain Adaptation

Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss

1 code implementation18 Nov 2019 Qian Wang, Toby P. Breckon

Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants.

Crowd Counting Image Classification

Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

no code implementations29 Oct 2019 Lingchen Zhao, Shengshan Hu, Qian Wang, Jianlin Jiang, Chao Shen, Xiangyang Luo, Pengfei Hu

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other.

An End-to-End Network for Co-Saliency Detection in One Single Image

no code implementations25 Oct 2019 Yuanhao Yue, Qin Zou, Hongkai Yu, Qian Wang, Song Wang

As a common visual problem, co-saliency detection within a single image does not attract enough attention and yet has not been well addressed.

Co-Salient Object Detection Saliency Prediction

The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagery

no code implementations25 Sep 2019 Neelanjan Bhowmik, Qian Wang, Yona Falinie A. Gaus, Marcin Szarek, Toby P. Breckon

This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.

Object Detection

NCLS: Neural Cross-Lingual Summarization

1 code implementation IJCNLP 2019 Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Cheng-qing Zong

Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.

Machine Translation Multi-Task Learning +1

Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

no code implementations30 Jul 2019 Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang

Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.

Image Registration

Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis

no code implementations7 Jul 2019 Dong Nie, Lei Xiang, Qian Wang, Dinggang Shen

To address this issue, we propose a simple but effective strategy, that is, we propose a dual-discriminator (dual-D) adversarial learning system, in which, a global-D is used to make an overall evaluation for the synthetic image, and a local-D is proposed to densely evaluate the local regions of the synthetic image.

Image Generation

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

no code implementations21 May 2019 Xuhua Ren, Lichi Zhang, Sahar Ahmad, Dong Nie, Fan Yang, Lei Xiang, Qian Wang, Dinggang Shen

In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to.

Brain Tumor Segmentation Scene Segmentation +1

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

no code implementations6 May 2019 Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu

Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.

Computed Tomography (CT) Dictionary Learning +1

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition

1 code implementation25 Mar 2019 Qian Wang, Penghui Bu, Toby P. Breckon

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain.

Generalized Zero-Shot Learning Unsupervised Domain Adaptation

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

2 code implementations6 Mar 2019 Qin Zou, Hanwen Jiang, Qiyu Dai, Yuanhao Yue, Long Chen, Qian Wang

Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction.

Lane Detection Time Series

Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks

no code implementations13 Feb 2019 Lei Bi, Yuyu Guo, Qian Wang, Dagan Feng, Michael Fulham, Jinman Kim

Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details.

Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

no code implementations25 Dec 2018 Lingchen Zhao, Qian Wang, Qin Zou, Yan Zhang, Yanjiao Chen

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc.

Image Classification Machine Translation +1

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

no code implementations3 Dec 2018 Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang song, Qian Wang, Hairong Qi

Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i. e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client.

Edge-computing Federated Learning

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks

1 code implementation20 Nov 2018 Qian Wang, Ning Jia, Toby P. Breckon

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.

Classification Data Augmentation +3

Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks

no code implementations19 Nov 2018 Xin Zhang, Qian Wang, Toby Breckon, Ioannis Ivrissimtzis

We present a method for reading digital data embedded in planar 3D printed surfaces.

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

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

1 code implementation1 Nov 2018 Qin Zou, Yanling Wang, Qian Wang, Yi Zhao, Qingquan Li

Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network.

Gait Recognition Person Identification

Block Matching Frame based Material Reconstruction for Spectral CT

no code implementations22 Oct 2018 Weiwen Wu, Qian Wang, Fenglin Liu, Yining Zhu, Hengyong Yu

Spectral computed tomography (CT) has a great potential in material identification and decomposition.

Computed Tomography (CT)

Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication

no code implementations25 Jul 2018 Qian Wang, Hang Li, Zhi Chen, Dou Zhao, Shuang Ye, Jiansheng Cai

In addition, we propose to use the convolutional recurrent neural network (CRNN)---a combination of the CNN and the RNN---to learn local and contextual information in CSI for user authentication.

Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

no code implementations24 Jul 2018 Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu

Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.

Computed Tomography (CT)

Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity

no code implementations28 Apr 2018 Xiaohuan Cao, Jianhua Yang, Li Wang, Zhong Xue, Qian Wang, Dinggang Shen

In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.

Image Registration

Medical Image Synthesis with Deep Convolutional Adversarial Networks

1 code implementation IEEE Transactions on Biomedical Engineering 2018 Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen, Fellow, IEEE

To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.

Image Generation

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

no code implementations13 Dec 2017 Weiwen Wu, Yanbo Zhang, Qian Wang, Fenglin Liu, Peijun Chen, Hengyong Yu

The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.

Computed Tomography (CT) Dictionary Learning

Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding

no code implementations15 Sep 2017 Qian Wang, Ke Chen

Our framework holistically tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for knowledge transfer.

Action Recognition Multi-Label Learning +2

Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image

no code implementations7 Sep 2017 Lei Xiang, Qian Wang, Xiyao Jin, Dong Nie, Yu Qiao, Dinggang Shen

After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN.

Computed Tomography (CT) Image Generation

Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree

no code implementations20 Jan 2017 Lei Lin, Qian Wang, Adel W. Sadek

Both the proposed method based on the FP tree algorithm, as well as the widely utilized, random forest method, were then used to identify the important variables or the Virginia dataset.

Variable Selection

Robust Gait Recognition by Integrating Inertial and RGBD Sensors

no code implementations31 Oct 2016 Qin Zou, Lihao Ni, Qian Wang, Qingquan Li, Song Wang

We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively.

Gait Recognition Person Identification

Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study

no code implementations26 Aug 2016 Qin Zou, Zheng Zhang, Qian Wang, Qingquan Li, Long Chen, Song Wang

Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus's influence is quantified by its corresponding accuracy in fashion classification.

Classification General Classification

Zero-Shot Visual Recognition via Bidirectional Latent Embedding

no code implementations7 Jul 2016 Qian Wang, Ke Chen

In the top-down stage, semantic representations of unseen-class labels in a given label vocabulary are then embedded to the same latent space to preserve the semantic relatedness between all different classes via our proposed semi-supervised Sammon mapping with the guidance of landmarks.

Action Recognition

Attentional Neural Network: Feature Selection Using Cognitive Feedback

1 code implementation NeurIPS 2014 Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang

Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture.

Feature Selection General Classification

Groupwise Registration via Graph Shrinkage on the Image Manifold

no code implementations CVPR 2013 Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen

Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images).

Image Registration

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