Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms
Abstract
Myeloid neoplasms (MN) present clinical and molecular heterogeneity and therefore a risk-adapted treatment strategy is mandatory. In MN, classification and prognostic tools based on clinical and morphologic criteria are being complemented by introducing genomic features. The clinical implementation of next-generation classifications and prognostic systems requires the availability of a robust methodological framework together with a solution to provide access to these technologies for clinicians.
Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization
Abstract
In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science.