Davide Boschetto, born in Padova in 1987, obtained his PhD at IMT School for Advanced Studies on December 2, 2016, defeding his PhD thesis titled "Quantitative methods for computer aided decision support systems in confocal laser endomicroscopy imaging of the gastrointestinal tract".
He previously received a B.Sc. degree in Biomedical Engineering in 2009 and a M.Sc. degree in Bioengineering in 2012, both from the University of Padova. For his MSc thesis, the use of Compressed Sensing in DSC-MRI was analyzed, with Prof. Enrico Grisan as Supervisor.
He enrolled in the XXVIIIth Cycle in the Computer, Decision, and Systems Science / Image Analysis (CDSS/IA) track in 2013.
At the beginning of his PhD he worked on problems regarding Myocardial Image Registration in Cardiac BOLD MRI stacks, performing time-series analysis to identify local differences in oxygenation among different myocardial territories, under the supervision of Prof. Sotirios Tsaftaris, with Cristian Rusu (formerly at IMT Lucca, now Universidade de Vigo) and Rita Morisi (IMT Lucca).
He then focused on developing a pipeline for a novel Computer-Aided Decision Support System (CADSS) for Endoscopy, particularly regarding the gastrointestinal (GI) tract, with high resolution datasets obtained from colonoscopies and gastroscopies, both with confocal endomicroscopy and white light / NBI endoscopies, in collaboration with the FAIR group in the University of Padova. His advisors were Prof. Enrico Grisan and Prof. Guido Caldarelli, and his thesis was reviewed by Dr. Stefano Realdon (Veneto Institute of Oncology) and Prof. Miguel Coimbra (University of Porto).
He won the AIDA-E (Analysis of Images to detect abnormalities in Endoscopy) challenge ("Gastric chromoendoscopy images in cancer surveillance") at IEEE International Symposium on Biomedical Imaging 2016, held in Prague, Czech Republic.
His main research interests are related to the field of data analytics (originating both from image processing and other sources), and include machine learning, pattern recognition, supervised and unsupervised classification, random forests and feature selection / dimensionality reduction.
He held a position of Data Scientist with Previnet S.p.A. in Preganziol, Treviso, working on:
He now holds the position of Deep Learning Specialist at Microtec Innovating Wook, in Mestre (Venice), working with the latest trends in Deep Learning with a computer vision-oriented focus using Keras and Tensorflow via Python.