133 citations · 135 across the 6 of their papers we have counts for
6 papers
Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Vandad Imani, Elaheh Moradi, Carlos Sevilla-Salcedo +2
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Gen…
Multi-Objective Genetic Algorithm for Multi-View Feature Selection
Vandad Imani, Carlos Sevilla-Salcedo, Elaheh Moradi +2
Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase…
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos +2
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missin…
Comparison of single and multitask learning for predicting cognitive decline based on MRI data
Vandad Imani, Mithilesh Prakash, Marzieh Zare +1
The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia.…
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review
Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh +4
Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In MRI, transfer le…
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
Juan Miguel Valverde, Vandad Imani, John D. Lewis +1
We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes…