Perugini M

No ORCID on file · 3 papers in corpus · active 2020-2022
2022
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.
Scientific reports ·doi:10.1038/s41598-022-12833-x

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centraliz…

2021
Automated detection of poor-quality data: case studies in healthcare.
Scientific reports ·doi:10.1038/s41598-021-97341-0

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requireme…

2020
Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.
Human reproduction (Oxford, England) ·doi:10.1093/humrep/deaa013

Study questionCan an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy?Summary answerWe have combined computer vision image processing methods and deep learning techniq…