Recent advances in multi-contrast magnetic resonance imaging (MRI) super-resolution (SR) have improved image quality under limited acquisition conditions. However, existing SR methods grapple with two fundamental bottlenecks. First, traditional diffusion models frequently generate images that deviate from ground-truth high-resolution images, resulting in structural distortions. Second, standard Transformer architectures often fail to accurately recover high-frequency details in images due to the inherent limitations of self-attention mechanisms. To surmount these challenges, we propose DPCAT, a synergistic SR framework that marries a Swin Transformer with a Denoising Diffusion Probabilistic Model (DDPM). Specifically, the diffusion module distils high-frequency priors that steer the SR reconstruction, while a Shifted Window Cross-Attention Transformer captures long-range dependencies between low-resolution inputs and reference images without inflating computational overhead. Complementarily, a Local Enhance Block refines spatially confined local features, and an Implicit Attention Network adaptively learns fusion weights to integrate complementary multi-contrast information. Extensive evaluations on the IXI and fastMRI datasets demonstrate that DPCAT establishes new state-of-the-art PSNR and SSIM across multiple upscaling factors, underscoring its potential to advance clinical MRI reconstruction.

Early structural changes in the hippocampus serve as critical biomarkers for the diagnosis of Alzheimer's disease (AD). However, traditional methods primarily concentrate on intra-regional features, overlooking the informative contributions of edge voxel regions. To address this limitation, we propose an Edge Voxel Feature Fusion Model (EVFFM) that integrates both internal and edge voxel features to enhance AD classification performance. Firstly, a multiscale edge mask is generated from the segmented hippocampal region using a stepwise binarization algorithm. Radiomics and deep learning features are then independently extracted from both the ROI and its edge regions. Sub-sequently, feature selection is conducted using a t-test, Spearman correlation analysis, and LASSO regression. Finally, the retained features of both areas are fused to construct the classification model. Evaluated on the ADNI dataset, EVFFM achieves an accuracy of 93.6 % ($\text{AUC}=0.973$) for AD vs. NC classification and 83.3 % ($\text{AUC}=0.822$) for MCI vs. NC. Furthermore, SHAP-based interpretability analysis confirmed the critical contribution of edge voxel features to the classification outcomes. The proposed method offers a novel approach to auxiliary diagnosis of AD.

In diagnosing Alzheimer’s Disease (AD), structural Magnetic Resonance Imaging (sMRI) is crucial, providing axial, coronal, and sagittal views for a comprehensive analysis of brain structures and pathological changes. Region of Interest (ROI) techniques in neuroimaging further allow detailed examination of structural and functional changes linked to AD. Additionally, certain SNPs have been identified as significant genetic markers for AD risk. Given the capabilities of Convolutional Neural Networks (CNNs) and Transformer models in handling multimodal data, this study introduces a novel hybrid network, DAResNet-ViT, for AD diagnosis. The model utilizes 24 key sMRI slices from each view per participant, combined with ROI features and genetic data, for classifying subjects. Data from two Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets were preprocessed and used to train and validate DAResNet-ViT. The model was evaluated through experiments focusing on AD diagnosis and the prediction of Mild Cognitive Impairment (MCI) conversion, with its effectiveness demonstrated in ablation studies and comparative experiments.

Magnetic resonance imaging (MRI) technology shows significant potential in predicting early pathological changes associated with Alzheimer's disease (AD). However, the complexity of MRI and their multidimensional characteristics pose challenges in model classification tasks, and single-scale features often fail to capture subtle changes in lesion areas effectively. To improve the accuracy of AD predictions, we propose a network model based on multi-scale features and a dual self-attention mechanism (MSDA). This model integrates depthwise separable convolutions with an improved self-attention mechanism, thereby enhancing the classification ability for AD. First, MRI undergo head motion correction, image alignment, and skull stripping to enhance the model's capability to extract features related to AD lesions. Second, we designed a multi-scale convolutional network structure that utilizes depthwise separable convolution kernels of varying sizes, allowing the network to effectively capture multi-scale feature information from MRI and accurately identify lesion areas. Finally, we introduced a dual self-attention module, which includes channel self-attention and spatial self-attention, further augmenting the model's ability to extract lesion features by learning the differences in features across different categories of MRI in both channel and spatial dimensions. Experimental results indicate that the MSDA network model demonstrates exceptional performance in classifying normal controls (NC) and AD within the ADNI dataset, outperforming existing models in classification accuracy, performance, and generalization capability. The accuracy, sensitivity, and specificity of the model reached 97.8%, 96.3%, and 99.4%, respectively.

Alzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, characterized by progressive memory decline, language impairment, and behavioral abnormalities. MRI has been widely utilized as a non-invasive technique for diagnosing AD. However, traditional cnn have limitations in extracting multi-scale lesion features, making it challenging to fully capture the local information and global dependencies of the lesion area. To overcome this issue, we propose a multi-scale feature attention network (MSFAN), which integrates multi-scale feature extraction (MSF), channel attention mechanism (CAM) and non-local block for AD classification. Specifically, the MSF module extracts local features at different scales through cascaded multi-scale convolution kernels; the CAM module adaptively reweights channel responses to emphasize critical lesion areas; and the non-local block captures long-range dependencies across different brain regions, thereby enhancing global feature representation. Experimental results show that MSFAN performs well compared to other cnn models in AD classification tasks, with accuracy, precision, recall and F1 score all significantly improved.

Alzheimer’s disease (AD) is a neurodegenerative disorder that primarily affects the elderly, characterized by cognitive decline and memory loss. Magnetic resonance imaging (MRI) plays a crucial role in the auxiliary diagnosis of AD. Recent studies have demonstrated that deep learning algorithms have achieved remarkable success in predicting AD. As an innovative feature extraction technology, ResNet has considerably advanced the application of deep learning in medical imaging. 3D convolutional neural networks (CNNs) can process three-dimensional MRI data directly, capturing the intricate three-dimensional characteristics of the brain’s structure. This study proposes a novel framework based on 3D ResNet, which integrates spatial and channel attention mechanisms to significantly enhance the sensitivity and accuracy of feature representation. Furthermore, we have incorporated non-local blocks within the residual blocks to enable the model to capture long-range dependencies. Experimental analysis indicates that our 3D ResNet model exhibits outstanding classification performance and can effectively support the clinical diagnosis of AD.

Alzheimer's disease (AD) is a neurodegenerative disorder that progresses in a slow and irreversible manner. Although many computer-aided methods have been used to diagnose AD, the issue of underutilization of detailed information and features persists. In this study, we propose a new AD diagnostic network (FPRes-Net) that can fully learn the rich information of 3D MRI slices by extracting multi-scale features and feature fusion. Firstly, in order to fully extract multi-scale information, a network structure combining ResNet-50 with feature pyramids was designed. Next, a feature fusion method was designed to reduce noise and increase the importance of important features. Finally, a visually interpretable method called Gradient-weighted Class Activation Mapping (Grad-CAM) was introduced to visualize important feature regions in AD diagnosis. Experimental analysis was conducted on the publicly accessible ADNI-1 dataset, and our proposed FPRes-Net model performed better than other advanced research methods, with an accuracy rate of 99.5%. Our proposed model can be effectively used for clinical diagnosis of AD.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.

Parkinson’s disease (PD) is a chronic neurodegenerative disease ranked second in the world. PD is often diagnosed using brain magnetic resonance imaging (MRI), which is a promising technique for PD biomarker development. However, it is difficult to focus on the pathogenic areas in the brain MRI of PD. Therefore, accurately capturing the characteristics of pathogenic areas has become an important issue. We propose a novel computational model (FF-MSPAM) for PD diagnosis by learning T2 weighted 3D-MRI slice features. First, in order to reduce parameters and accelerate training speed, a mixed network with ordinary convolution and separable convolution (OS-CNN) is designed. Next, VGG19 is applied for feature fusion to extract richer features. Finally, a multi-scale parallel attention mechanism (MSP-AM) was established to focus and aggregate spatial and channel features at different scales. The applicability of the proposed model was demonstrated using T2 weighted 3D-MRI slices of 168 subjects obtained from publicly available database. We have achieved classification accuracy of up to 98% in the differential diagnosis of PD. The experiment shows that our method is successful. Good results were obtained in PD diagnosis task and compared with advanced research models. Our model can be used for the diagnosis and prognosis of PD.

Individuals with the Alzheimer’s disease (AD) go through multiple stages from health to illness. The pathogenesis of AD remains uncertain, and there may be different biomarkers in different diagnostic groups. In the field of brain imaging genetics, it has become a significance challenge to utilize the brain genotype-phenotype correlations to probe the pathogenesis of AD. To solve these problems, a novel approach named bi-multitask regularized sparse canonical correlation analysis and logistic regression (BRSCCALR) is proposed, which can identify AD related biomarkers and classify subjects. Specifically, multitask sparse canonical correlation analysis focuses on learning genotype-phenotype associations. Yet the newly constructed multitask regularized logistic regression that prevents overfitting is responsible for identifying diagnosis-specific biomarkers. In addition, the connectivity-based penalty term is also introduced to enrich the prior information and enhance the biological significance of the method. Under the five-fold cross-validation experiment, the proposed method is compared with several state-of-the-art methods on a real brain imaging genetic dataset. The canonical correlation coefficients demonstrate that BRSCCALR method achieves outstanding performance. Finally, the learned biomarkers are applied to the classification experiment, and results show that the biomarkers are valid.

Deep-learning methods (especially convolutional neural networks) using magnetic resonance imaging (MRI) data have been successfully applied to computer-aided diagnosis of Parkinson's Disease (PD). Early detection and prior care may help patients improve their quality of life, although this neurodegenerative disease has no known cure. In this study, we propose a FResnet18 model to classify MRI images of PD and Health Control (HC) by fusing image texture features with deep features. First, Local Binary Pattern and Gray-Level Co-occurrence Matrix are used to extract the handcrafted features. Second, the modified ResNet18 network is used to extract deep features. Finally, the fused features are classified by Support Vector Machine. The classification accuracy rate for MRI images reaches 98.66%, and the findings demonstrate that the model can successfully differentiate between PD and HC. The suggested FResnet18 provides greater performance compared with existing approaches, and it is shown through extensive experimental findings on the Parkinson's Disease Progression Markers Initiative data set that feature fusion may improve classification performance.

Alzheimer's disease (AD) is the main cause of dementia in the elderly. To date, it remains largely unknown whether and how dynamic characteristics of the functional networks differ from cognitively normal (CN) to AD. Here, we propose an AD dynamic network complexity intelligent detecting algorithm based on visibility graph. The focal regions that caused the dynamic abnormality of the connection mode were intelligently detected by creating a dynamic complexity network on the basis of the dynamic functional network. The results showed that the brain areas with different dynamic complexity gradually shifted from the frontal lobe to the temporal lobe and the occipital lobe. This was significantly related to the disorder of clinical patients from mood to memory and language. The increased dynamic complexity illustrates the compensatory effect of the brain area of AD lesions. In addition, the small-world topological properties of the dynamic complexity network have significant differences from CN to AD. To the best of our knowledge, this is the first time that such a concept is proposed. Our method of intelligently detecting the complexity of AD dynamic network provides new insights for understanding the internal dynamic mechanism of AD brain.

Many Internet service providers are still using traditional password strength evaluation methods, resulting in user passwords being vulnerable to social engineering attacks. We believe that the password strength evaluation method based on sensitive personal information has great research value for improving the security of password authentication system. In this paper, we use the structure segmentation algorithm and the bidirectional matching algorithm to investigate how users' personal information is used in passwords. Then, we present a sensitivity personal information coverage evaluation function that represents the correlation between users' password and their personal information. Finally, a password strength evaluation method based on sensitive personal information is proposed. This method is composed of three stages: preprocessing stage, prediction dictionary generation stage and password strength evaluation stage.