Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET.

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.

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy.

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned.

Editorial for Special Issue "Fine Art Pattern Extraction and Recognition".

Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies [...