Neural memory plasticity for medical anomaly detection

doi:10.1016/j.neunet.2020.04.011

Paper

Neural memory plasticity for medical anomaly detection

Published Apr 18, 2020 · Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal

Neural networks : the official journal of the International Neural Network Society

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34

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Abstract

Abstract removed due to Elsevier request; this does not indicate any issues with the research. Click the full text link above to read the abstract and view the original source.

Study Snapshot

Key takeawayThe proposed neural memory access mechanism, combining static and dynamic connection weights, outperforms current state-of-the-art in medical anomaly detection tasks like abnormal EEG identification, MRI tumor type classification, and schizophrenia risk detection in children.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Neural memory plasticity for medical anomaly detection

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