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Found 8 papers, 1 papers with code
Salp Swarm Optimization: a Critical Review
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.
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
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.
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
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.
Investigating the performance of multi-objective optimization when learning Bayesian Networks
Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables.
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Prostate cancer is the most common cancer among US men.
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
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.
Assisting clinical practice with fuzzy probabilistic decision trees
In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice.
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
In order to evaluate the impact of interpretations on perceived trust, explanation satisfaction attributes are rated by MEs through a survey.
