Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Background: Automated anomaly detection is an important tool that has been developed for many real-world applications, including security systems, industrial inspection, and medical diagnostics. Despite extensive use of machine learning for anomaly detection in these varied contexts, it is challengi...

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Bibliographic Details
Main Authors: Philip Zehnder, Jeffrey Feng, Reina N. Fuji, Ruth Sullivan, Fangyao Hu
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Journal of Pathology Informatics
Subjects:
Anomaly detection
Digital pathology
Deep learning
Toxicological pathology
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353922006964

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http://www.sciencedirect.com/science/article/pii/S2153353922006964

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