Alert button
Picture for Marco S. Nobile

Marco S. Nobile

Alert button

Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model

Jul 15, 2023
Mohsen Abbaspour Onari, Isel Grau, Marco S. Nobile, Yingqian Zhang

Figure 1 for Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Figure 2 for Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Figure 3 for Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Figure 4 for Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Viaarxiv icon

Assisting clinical practice with fuzzy probabilistic decision trees

Apr 26, 2023
Emma L. Ambags, Giulia Capitoli, Vincenzo L' Imperio, Michele Provenzano, Marco S. Nobile, Pietro Liò

Figure 1 for Assisting clinical practice with fuzzy probabilistic decision trees
Figure 2 for Assisting clinical practice with fuzzy probabilistic decision trees
Figure 3 for Assisting clinical practice with fuzzy probabilistic decision trees
Figure 4 for Assisting clinical practice with fuzzy probabilistic decision trees
Viaarxiv icon

Salp Swarm Optimization: a Critical Review

Jun 03, 2021
Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni

Figure 1 for Salp Swarm Optimization: a Critical Review
Figure 2 for Salp Swarm Optimization: a Critical Review
Figure 3 for Salp Swarm Optimization: a Critical Review
Viaarxiv icon

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

Apr 17, 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

Figure 1 for USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Figure 2 for USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Figure 3 for USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Figure 4 for USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Viaarxiv icon

CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study

Mar 29, 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

Figure 1 for CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Figure 2 for CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Figure 3 for CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Figure 4 for CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Viaarxiv icon

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

Oct 23, 2018
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti, Alex Graudenzi

Figure 1 for Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Figure 2 for Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Figure 3 for Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Figure 4 for Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Viaarxiv icon

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks

Aug 03, 2018
Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti

Figure 1 for Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks
Figure 2 for Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks
Figure 3 for Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks
Figure 4 for Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks
Viaarxiv icon

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

Mar 08, 2017
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti

Figure 1 for Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Figure 2 for Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Figure 3 for Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Figure 4 for Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Viaarxiv icon