Search Results for author: Marco S. Nobile

Found 6 papers, 1 papers with code

Salp Swarm Optimization: a Critical Review

1 code implementation3 Jun 2021 Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni

In the crowded environment of bio-inspired population-based meta-heuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

no code implementations17 Apr 2019 Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga

The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.

Investigating the performance of multi-objective optimization when learning Bayesian Networks

no code implementations3 Aug 2018 Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables.

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

no code implementations8 Mar 2017 Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage.

Global Optimization

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

no code implementations8 Mar 2017 Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti, Alex Graudenzi

In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena.

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