Massimo Filippi

Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D...

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which...

Intrinsic-Dimension Analysis for Guiding Dimensionality Reduction in Multi-Omics Data

Dynamic Functional Connectivity For The Classification Of Multiple Sclerosis Phenotype: A Hidden Markov Model Approach

An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis

Encoding Brain Networks Through Geodesic Clustering of Functional Connectivity for Multiple Sclerosis Classification

Ambient Assisted Living and ageing: Preliminary results of RITA project

A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonance Images

Combining DTI and MRI for the Automated Detection of Alzheimer's Disease Using a Large European Multicenter Dataset

Semi-Supervised Diffusion Model for Brain Age Prediction

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To...

Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent years. However, most methods tend to be static, that is,...