no code implementations • 15 Jul 2023 • Mohsen Abbaspour Onari, Isel Grau, Marco S. Nobile, Yingqian Zhang
In order to evaluate the impact of interpretations on perceived trust, explanation satisfaction attributes are rated by MEs through a survey.
no code implementations • 16 Apr 2023 • Emma L. Ambags, Giulia Capitoli, Vincenzo L' Imperio, Michele Provenzano, Marco S. Nobile, Pietro Liò
In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice.
1 code implementation • 3 Jun 2021 • Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni
In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.
no code implementations • 17 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.
no code implementations • 29 Mar 2019 • Leonardo Rundo, Changhee Han, Jin Zhang, Ryuichiro Hataya, Yudai Nagano, Carmelo Militello, Claudio Ferretti, Marco S. Nobile, Andrea Tangherloni, Maria Carla Gilardi, Salvatore Vitabile, Hideki Nakayama, Giancarlo Mauri
Prostate cancer is the most common cancer among US men.
no code implementations • 3 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.
no code implementations • 8 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.
no code implementations • 8 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.