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Found 5 papers, 3 papers with code
Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data
Clinical neuroimaging has recently witnessed explosive growth in data availability which brings studying heterogeneity in clinical cohorts to the spotlight.
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data
Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders.
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data
Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge.
PROMISSING: Pruning Missing Values in Neural Networks
In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know).
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
Altogether, we demonstrate a scalable approach for anomaly detection of complex brain disorders based on cortical abnormalities.
