8 citations · 24 across the 8 of their papers we have counts for
8 papers
Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images
Linchen Qian, Jiasong Chen, Linhai Ma +3
Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health conce…
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI
Jiasong Chen, Linchen Qian, Linhai Ma +3
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measur…
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection
Linhai Ma, Liang Liang
It is known that Deep Neural networks (DNNs) are vulnerable to adversarial attacks, and the adversarial robustness of DNNs could be improved by adding adversarial noises to trainin…
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can…
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
Liang Liang, Linhai Ma, Linchen Qian +1
Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks. However, such performance is only guarantee…
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks
Linhai Ma, Liang Liang
Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general and effective strategy to improve DNN robustness (i.e., accuracy on noisy data)…
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…