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Found 5 papers, 0 papers with code
Pilot Study to Discover Candidate Biomarkers for Autism based on Perception and Production of Facial Expressions
Results: We identify one candidate ET biomarker (percentage gaze duration to the face while mimicking static 'disgust' expression) and 14 additional DVs of interest for future study, including 4 ET DVs, 5 DVs related to VT AU activation, and 4 DVs related to AU asymmetry in VT. Based on a power analysis, we provide sample size recommendations for future studies.
Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement
To measure subjects' AUs in response to CADyFACE, we propose a novel Beta-guided Correlation and Multi-task Expression learning neural network (BeCoME-Net) for multi-label AU detection.
Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients
To our knowledge, this study is the first to investigate the potential of conventional ra-diomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods.
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features
We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space for robust classification of either domain.
Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework
Our extensive analyses involving six tabular data sets, up to 80% missingness, and three missingness types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL.
