Detecting anomalies from liquid transfer videos in automated laboratory setting

Author:

Sarker Najibul Haque,Hakim Zaber Abdul,Dabouei Ali,Uddin Mostofa Rafid,Freyberg Zachary,MacWilliams Andy,Kangas Joshua,Xu Min

Abstract

In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting.

Funder

National Institutes of Health

National Science Foundation

Pittsburgh Foundation

Mark Foundation For Cancer Research

Advanced Micro Devices

Publisher

Frontiers Media SA

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Molecular Biology,Biochemistry

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