Abstract With the rapid adoption of smart healthcare systems, medical image distribution is expected to become increasingly prevalent. However, such distribution faces significant threats of privacy breaches. Although conventional encryption schemes can prevent unauthorized access during transmission, they cannot control the illegal redistribution of decrypted content. To prevent medical images from being accessed by unauthorized personnel, we propose a dual-level security scheme that integrates robust watermarking with selective encryption for smart healthcare environments. The primary objective is to protect medical images throughout their entire lifecycle. The key innovation of our approach lies in its ability to perform watermarking operations directly on encrypted content. Additionally, the scheme is designed to satisfy diverse security requirements across various medical scenarios. This dual-level security mechanism enables tracing of medical content usage and controlling unauthorized redistribution. Comprehensive experimental evaluations and security analyses demonstrate that our method outperforms existing approaches in computational efficiency while maintaining comparable security levels. This research contributes a novel methodology for end-to-end protection of medical images and holds significant implications for multimedia security in healthcare contexts.

Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability to model complex causal relationships between brain regions. In this paper, a brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. To our best knowledge, the proposed brain diffuser is the first generative model to apply diffusion models to the application of generating and analyzing multimodal brain networks. Specifically, the BDHT leverages structural connectivity to guide the reverse processes in an efficient way. It makes the denoising process more reliable and guarantees effective connectivity estimation accuracy. To improve denoising quality, the hierarchical denoising transformer is designed to learn multi-scale features in topological space. By stacking the multi-head attention and graph convolutional network, the graph convolutional transformer (GraphConformer) module is devised to enhance structure-function complementarity and improve the ability in noise estimation. Experimental evaluations of the denoising diffusion model demonstrate its effectiveness in estimating effective connectivity. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches. Moreover, the proposed model can identify altered directional connections and provide a comprehensive understanding of parthenogenesis for MCI treatment.Note to Practitioners—Diagnosing MCI allows for timely intervention and treatment measures to potentially slow down or even halt further cognitive decline. Exploring causal relations between brain regions enables a better understanding of pathogenic mechanisms and the development of effective biomarkers for MCI diagnosis. The current practice heavily relies on the software to analyze MCI causality, leading to large computing errors and degrading MCI analysis performance because of different parameter settings. This work aims to provide a unified framework for the estimation of brain effective connectivity using generative artificial intelligence. Due to their ability to generate high-quality samples, diffusion models have demonstrated remarkable performance in cross-modal medical image synthesis through iterative denoising processes. Our model provides a new insight into how to transform four-dimensional functional magnetic resonance imaging into effective connectivity without relying on software toolkits. The proposed model achieves good disease prediction performance and identifies altered directional connections that may be potential biomarkers for MCI treatment. Our work enables practitioners to develop deep learning model-based medical tools to assist clinicians with disease diagnosis and pathological analysis in an efficient way. Our work can also extend to the intelligently assisted diagnosis of other neurological diseases.

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.