This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive because it offers the possibility of immediate corrective action, early termination, and redesign of assays that may require many hours or days to execute. The proposed approach employs assay-specific image processing within an assay-independent framework for distributed control, machine learning, and anomaly reporting. Specifically, we exploit coarse-grained parallelism to distribute image processing over several computing nodes while efficiently aggregating sufficient statistics across nodes. This architecture also allows us to easily handle geographically-distributed data sources. Our results from two applications, adipocyte quantitation and neurite growth estimation, confirm that this online approach to anomaly detection is feasible, efficient, and accurate.
['Adam Goode', 'Rahul Sukthankar', 'Lily B. Mummert', 'Mei Chen', 'Jeffrey Saltzman', 'David Ross', 'Stacey Szymanski', 'Anil Tarachandani', 'Mahadev Satyanarayanan']
Distributed online anomaly detection in high-content screening