Computer Science Department
School of Computer Science, Carnegie Mellon University


Distributed Online Anomaly Detection in
High-Content Screening

Adam Goode, Rahul Sukthankar*, Lily Mummert*, Mei Chen*,
Jeffrey Saltzman**, David Ross**, Stacey Szymanski**,
Anil Tarachandani**, M. Satyanarayanan

December 2007


Keywords: Anomaly detection, biomedical image processing, distributed database searching, high-content microscopy, OpenDiamond® search platform

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.

9 pages

*Intel Research Pittsburgh
**Merck & Co., Inc.

Return to: SCS Technical Report Collection
School of Computer Science

This page maintained by