Projects

Hybrid Generative AI for Histopathology ISCRA-B project supported by LEONARDO HPC at CINECA. The goal of this project is to improve the accuracy of early detection of high-risk colorectal adenomas in whole-slide images. The challenges posed by this task are many, from the difficulty of collecting large enough datasets for training, to the computational requirements of processing whole-slide images, which usually reach resolutions in the order of tens of billions of pixels.

DeepHealth (EC-IA, H2020, ICT-2018-11): Deep-Learning and HPC to Boost Biomedical Applications for Health (2019, 36 months). DeepHealth project is funded by the EC under the topic ICT-11-2018-2019 “HPC and Big Data enabled Large-scale Test-beds and Applications”. The aim of DeepHealth is to offer a unified framework completely adapted to exploit underlying heterogeneous HPC and Big Data architectures; and assembled with state-of-the-art techniques in Deep Learning and Computer Vision. In particular,the project will combine High-Performance Computing (HPC) infrastructures with Deep Learning (DL) and Artificial Intelligence (AI) techniques to support biomedical applications that require the analysis of large and complex biomedical datasets and thus, new and more efficient ways of diagnosis, monitoring and treatment of diseases.