Explainable deep learning in healthcare: A methodological survey from an attribution view
The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …
unprecedented technical advances in deep learning (DL) have sparked a surge of research …
Deep learning models for electrocardiograms are susceptible to adversarial attack
Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial
devices, necessitating the development of automated interpretation strategies. Recently …
devices, necessitating the development of automated interpretation strategies. Recently …
Adversarial attacks against lidar semantic segmentation in autonomous driving
Today, most autonomous vehicles (AVs) rely on LiDAR (Light Detection and Ranging)
perception to acquire accurate information about their immediate surroundings. In LiDAR …
perception to acquire accurate information about their immediate surroundings. In LiDAR …
Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment
This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia
classification from ECG signal inputs. ArrhyMon targets to detect and classify six different …
classification from ECG signal inputs. ArrhyMon targets to detect and classify six different …
TSadv: Black-box adversarial attack on time series with local perturbations
W Yang, J Yuan, X Wang, P Zhao - Engineering Applications of Artificial …, 2022 - Elsevier
Deep neural networks (DNNs) for time series classification have potential security concerns
due to their vulnerability to adversarial attacks. Previous work that perturbs time series …
due to their vulnerability to adversarial attacks. Previous work that perturbs time series …
Geoecg: Data augmentation via wasserstein geodesic perturbation for robust electrocardiogram prediction
There has been an increased interest in applying deep neural networks to automatically
interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with …
interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with …
Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression
X Chen, Y Si, Z Zhang, W Yang, J Feng - Sensors, 2024 - mdpi.com
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of
electrocardiogram (ECG) signals. However, research has shown that DNNs are highly …
electrocardiogram (ECG) signals. However, research has shown that DNNs are highly …
Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems
The adoption of machine-learning-enabled systems in the healthcare domain is on the rise.
While the use of ML in healthcare has several benefits, it also expands the threat surface of …
While the use of ML in healthcare has several benefits, it also expands the threat surface of …
CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training
J Shao, S Geng, Z Fu, W Xu, T Liu, S Hong - Biomedical Signal Processing …, 2024 - Elsevier
In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders.
Owing to the development of technology and the increase in health awareness, ECG signals …
Owing to the development of technology and the increase in health awareness, ECG signals …
Efficient Time-Series Data Delivery in IoT with Xender
Large amounts of time-series data need to be continually delivered from IoT devices to the
cloud for real-time data analytics. The data delivery process is intrinsically slow and costly …
cloud for real-time data analytics. The data delivery process is intrinsically slow and costly …