206 citations · 522 across the 32 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…