Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease’s causes.

Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to $9\%$ on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to $11\%$). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort.

The main purpose of a quantum key distribution network is to provide secret keys to any users or applications requiring a high level of security, ideally such as to offer the best protection against any computational attack, even of a quantum nature. The keys shared through a point-to-point link between a source and a detector using a quantum key distribution protocol can be proven information-theoretically secure based on the quantum information theory. However, evaluating the security of a quantum key distribution network, especially if it is based on relay nodes, goes far beyond the quantum security of its single quantum links, involving aspects of conventional security for devices and their communication channels. In this contribution, we perform a rigorous threat analysis based on the most recent recommendations and practical network deployment security issues. We show that, at least in the current state of our understanding of quantum cryptography, quantum key distribution networks can only offer computational security and that their security in practical implementations in the shorter term requires resorting to post-quantum cryptography.