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Found 8 papers, 7 papers with code
Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images
The deformed template reveals the lumbar spine geometry in an image.
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI
In this work, we proposed SymTC, an innovative lumbar spine MR image segmentation model that combines the strengths of Transformer and Convolutional Neural Network (CNN).
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection
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 training data (e. g., the standard adversarial training (SAT)).
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart.
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks.
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length
Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification, a life-critical application.
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks
However, adversarial training samples with excessive noises can harm standard accuracy, which may be unacceptable for many medical image analysis applications.
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial attacks: subtle changes in input of a DNN can lead to a wrong classification output with high confidence.
