Theses

• [suggested: MASTER] Data Analysis from Smartphone Application for Quality of Life Assessment
  Context: Fatigue is a disabling symptom in 65-97% of patients with multiple sclerosis (MS). Fatigue is the presenting symptom in one third of MS patients and 15-40% describe fatigue as their most severe symptom. Despite its clinical significance, the pathophysiology of fatigue is not well understood. In this context, a mobile app was developed and used in the context of a prospective study designed to investigate brain MRI correlates of treatment-resistant fatigue in patients with MS. 56 MS patients used the application, and 47/56 (84%) completed all questionnaires of the app.

Objective: To identify temporal fatigue/depression/anxiety/pain/actigraphic patterns which can differentiate the above-mentioned MS patients into mechanistically meaningful subgroups using cluster (eg, independent component) analysis methods.

Supervisors: F. Calimeri, A. Marzullo
Partners: Harvard Medical School, Boston (USA)

• [suggested: BACHELOR] Front- and Back-end Development for SPINE
  The “Structured Planning and Implementation of New Explorations” (SPINE) is a web platform designed the to help researches in the design and management the whole lifecycle of a research experiment involving MRI images and clinical data. SPINE aims to answer scientific questions by providing all the required tools to the scientist, enabling experts, such as radiologist, to integrate their knowledge, computer scientist to insert image processing procedures, and statisticians to input models for the analysis of the results, all in the same place. SPINE can also be seen as a virtual laboratory. This virtual aspect of SPINE enables the use of crowdsourcing of knowledge and by consequence can be used in fields such as teaching environments or citizen-science.
  Supervisor: F. Calimeri, A. Marzullo
  Partners: Harvard Medical School, Boston (USA)

• F. Filice, "Un Approccio basato su Answer Set Programming per data augmentation in ambito oculistico"

• M. Scarfone, "Deep Learning and Answer Set Programming in parallel way for the semantic segmentation of medical images"

• G. Berardini, "Enabling Deep Learning Experiments in a Web-based Platform for Medical Image Analysis"

• V. Corbetta, "Planning, optimisation and classification of 3D trajectories for robotic steerable needles in keyhole neurosurgery with a deductive reasoning approach"

• M. Donato, "A new way for medical research: advancements in SPINE"

• F. Doria, "A web-app application for supporting Hybrid AI approaches"

• C. Mesa Aparicio, "Using Generative Adversarial Networks for the generation of biomedical data"

• R. Bisignano, "Mask R-CNN for nuclei instance segmentation"

• L. Cinelli, "Neurological Disorders Analysis by Mixing Logic Programming and Neural Networks"

• G. Burza, "Improved pseudo CT synthesis using Gaussian Process Regression"

• L. Brusco, "Neural Networks for automatic ECG classification"

• G. Melissari, "Analysis of brain structural connectivity variations in multiple sclerosis clinical profiles"

• D. Q. Toản, "Investigating Machine Learning Techniques for Disability Status Score Prediction in Multiple Sclerosis Patients"

• S. Monetti, "A Cloud Based Platform to process and analyze next generation sequencing data from patients with neurological diseases"

• F. Eberhard, "Confidence Based Ensemble Modeling in Medical Imaging Using Graphs"

• L. Kammerer, "Confidence-Based Ensemble Modeling in Medical Data Mining"

• S. Isabella, "Brain.io: a Serious Game for Unsupervised Damag Segmentation of Neurodegenerative Diseases"

• D. Pezzolla, "Kafka: An Automated Tool for MRI Anonymization"

• M. Caracciolo, "BioHIPI: un framework basato su Hadoop per l’Analisi di Immagini Biomediche"

• C. Stamile, "Definizione di modelli innovativi per la predizione dell'evoluzione degli edemi cerebrali"

• P. Bruno, "Design and implementation of analytical tools for biomedical data"

• A. Marzullo, "Retinal fundus image processing: a deep learning based approach"

• A. Marzullo, "A web-based framework for the design of novel tools for the analysis of biomedical images"