206 citations · 522 across the 32 of their papers we have counts for
32 papers
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Xin Zhang, Liangxiu Han, Lianghao Han +3
Structural magnetic resonance imaging (sMRI) can identify subtle brain changes due to its high contrast for soft tissues and high spatial resolution. It has been widely used in dia…
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
Lianyu Wang, Meng Wang, Daoqiang Zhang +1
Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain. Reduc…
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
Samson B. Akintoye, Liangxiu Han, Huw Lloyd +4
Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performan…
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Mengting Xu, Tao Zhang, Zhongnian Li +1
Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Projected Gradient Descent (PGD) attack has been demonstrate…
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack
Mengting Xu, Tao Zhang, Daoqiang Zhang
Deep neural networks are discovered to be non-robust when attacked by imperceptible adversarial examples, which is dangerous for it applied into medical diagnostic system that requ…
CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images
Xin Zhang, Liangxiu Han, Tam Sobeih +6
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it…
A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning
Samson B. Akintoye, Liangxiu Han, Xin Zhang +2
Recently, Deep Neural Networks (DNNs) have recorded great success in handling medical and other complex classification tasks. However, as the sizes of a DNN model and the available…
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Mengting Xu, Tao Zhang, Zhongnian Li +2
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown d…
Improving the Certified Robustness of Neural Networks via Consistency Regularization
Mengting Xu, Tao Zhang, Zhongnian Li +1
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to b…
Transport based Graph Kernels
Kai Ma, Peng Wan, Daoqiang Zhang
Graph kernel is a powerful tool measuring the similarity between graphs. Most of the existing graph kernels focused on node labels or attributes and ignored graph hierarchical stru…
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang +2
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing diff…
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
Yuang Shi, Chen Zu, Mei Hong +6
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image ana…
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella +1
Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by differen…
Ordinal Pattern Kernel for Brain Connectivity Network Classification
Kai Ma, Biao Jie, Daoqiang Zhang
Brain connectivity networks, which characterize the functional or structural interaction of brain regions, has been widely used for brain disease classification. Kernel-based metho…
An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
Xin Zhang, Liangxiu Han, Wenyong Zhu +2
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided…
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Li Zhang, Mingliang Wang, Mingxia Liu +1
Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), a…
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
Liang Sun, Zhanhao Mo, Fuhua Yan +14
Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed…
Geometric Interpretation of Running Nyström-Based Kernel Machines and Error Analysis
Weida Li, Mingxia Liu, Daoqiang Zhang
Recently, Nyström method has proved its prominence empirically and theoretically in speeding up the training of kernel machines while retaining satisfactory performances and accura…
Supervised Hyperalignment for multi-subject fMRI data alignment
Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han +1
Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Reson…
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Weida Li, Mingxia Liu, Fang Chen +1
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brai…
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
Zhongnian Li, Tao Zhang, Peng Wan +1
Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of s…
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Xiaoliang Sheng, Muhammad Yousefnezhad, Tonglin Xu +2
Representational Similarity Analysis (RSA) aims to explore similarities between neural activities of different stimuli. Classical RSA techniques employ the inverse of the covarianc…
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
Muhammad Yousefnezhad, Daoqiang Zhang
In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standa…
Gradient Hyperalignment for multi-subject fMRI data alignment
Tonglin Xu, Muhammad Yousefnezhad, Daoqiang Zhang
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make…