1 code implementation • 8 Nov 2022 • Chiara Marzi, Marco Giannelli, Andrea Barucci, Carlo Tessa, Mario Mascalchi, Stefano Diciotti
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques.
1 code implementation • Scientific Reports 2021 • Ekin Yagis, Selamawet Workalemahu Atnafu, Alba García Seco De Herrera, Chiara Marzi, Riccardo Scheda, Marco Giannelli, Carlo Tessa, Luca Citi, Stefano Diciotti
In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD).
1 code implementation • Scientific Reports 2020 • Chiara Marzi, Marco Giannelli, Carlo Tessa, Mario Mascalchi, Stefano Diciotti
We compared four different strategies, including two a priori selections of the interval of spatial scales, an automated selection of the spatial scales within which the cerebral cortex manifests the highest statistical self-similarity, and an improved approach, based on the search of the interval of spatial scales which presents the highest rounded R2adj coefficient and, in case of equal rounded R2adj coefficient, preferring the widest interval in the log–log plot.
