CREMONESI , Francesco

Federated learning allows for the training of machine learn- ing models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strate- gies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically performed locally....

IntroductionThe emergence of AI models for the analysis of 18FDG PET images has opened the way for automatic quantification of clinically-relevant parameters in metastatic cancer patients such as e.g. the number, metabolic volume, and dispersion of lesions. However, current approaches lack specificity as they do not provide information about...

Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative...