Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. Recent studies demonstrate that examples are not equally important to the final robustness of models during AT, that is, the so-called hard examples that can be attacked easily exhibit more influence than robust examples on the final robustness. Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model. However, defining effective heuristics to search for hard examples is still difficult. In this article, inspired by the information bottleneck (IB) principle, we uncover that an example with high mutual information of the input and its associated latent representation is more likely to be attacked. Based on this observation, we propose a novel and effective adversarial training method (InfoAT). InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models. Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.

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 requires high reliability. However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks. The pre-processing methods (e.g., random resizing, compression) may lead to the loss of the small lesions feature in the medical image. Retraining the network on the augmented data set is also not practical for medical models that have already been deployed online. Accordingly, it is necessary to design an easy-to-deploy and effective defense framework for medical diagnostic tasks. In this paper, we propose a Robust and Retrain-Less Diagnostic Framework for Medical pretrained models against adversarial attack (i.e., MedRDF). It acts on the inference time of the pretrained medical model. Specifically, for each test image, MedRDF firstly creates a large number of noisy copies of it, and obtains the output labels of these copies from the pretrained medical diagnostic model. Then, based on the labels of these copies, MedRDF outputs the final robust diagnostic result by majority voting. In addition to the diagnostic result, MedRDF produces the Robust Metric (RM) as the confidence of the result. Therefore, it is convenient and reliable to utilize MedRDF to convert pretrained non-robust diagnostic models into robust ones. The experimental results on COVID-19 and DermaMNIST datasets verify the effectiveness of our MedRDF in improving the robustness of medical diagnostic models.

This paper presents the first study on Sign-aware Perturbations Regression (SaPR), where the observed response variables contain the aware sign (negative or positive) perturbations.In order to predict the non-perturbation response variables, we propose a novel parameter estimator SZOM (i.e.,Setting Zero Operator Method), which aims at taking full advantage of the aware perturbations information to correct the mistake values in the estimation process with computationally efficiency.In this paper, the two aspects of theoretical analysis are proposed to deeply understand our method.Firstly, we establish the perturbation parameter error upper bound and prove consistency guarantee in the linear regression scenario.Secondly, we introduce the generalization error bound for the proposed SZMO, which indicates that the error bound is related to the value and the number of negative and positive perturbations.The effectiveness of the proposed approach is well validated by the experimental results on both synthetic and real datasets.

In Compressive Sensing MRI (CS-MRI), measurement matrix learning has been developed as a promising method for measurement matrix designing. Research on MRI measurement task suggests that Relative 2-Norm Error (RLNE) of measurement images is imbalanced. However, current learning-based investigations suffer from the lack of probing imbalanced characteristic on measurement matrix learning. In this paper, we propose a novel Measurement Matrix Learning via Correlation Reweighting (MML-CR) approach for exploring and solving this problem by optimizing reweighted model.Specifically,we introduce a reweighting expected minimization model to obtain an essential measurement matrix in k-space. Besides, we propose an example correlation regularizer to prevent trivial solution for learning weights. Furthermore, we present an alternating solution and perform convergence analysis for the optimization. We also demonstrate quantitative and qualitative experimental results which show that our algorithm outperforms several state-of-art measurements methods. Compared with conventional methods, MML-CR achieves better performance on universal task.

Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of structure information of MRI images that is crucial for clinical diagnosis. To tackle this problem, we propose the Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at both local and global scale. SEGAN defines a new structure regularization called Patch Correlation Regularization (PCR) which allows for efficient extraction of structure information. In addition, to further enhance the ability to uncover structure information, we propose a novel generator SU-Net by incorporating multiple-scale convolution filters into each layer. Besides, we theoretically analyze the convergence of stochastic factors contained in training process. Experimental results show that SEGAN is able to learn target structure information and achieves state-of-theart performance for CS-MRI reconstruction.