{"doi":"10.3389/fmolb.2023.1147514","title":"Detecting anomalies from liquid transfer videos in automated laboratory setting","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.","journal":"Frontiers in Molecular Biosciences","year":2023,"id":365561,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5129,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1121871,"name":"Zaber Abdul Hakim","orcid":null,"position":1,"is_corresponding":false},{"id":1121423,"name":"Ali Dabouei","orcid":"0000-0002-1084-6224","position":2,"is_corresponding":false},{"id":741562,"name":"Mostofa Rafid Uddin","orcid":null,"position":3,"is_corresponding":false},{"id":307539,"name":"Zachary Freyberg","orcid":"0000-0001-6460-0118","position":4,"is_corresponding":false},{"id":1121872,"name":"Andy MacWilliams","orcid":null,"position":5,"is_corresponding":false},{"id":1121424,"name":"Joshua Kangas","orcid":"0000-0002-6742-5113","position":6,"is_corresponding":false},{"id":383213,"name":"Min Xu","orcid":"0000-0002-0881-5891","position":7,"is_corresponding":false},{"id":1121870,"name":"Najibul Haque Sarker","orcid":null,"position":0,"is_corresponding":true}],"reference_count":82,"raw_metadata":null,"created_at":"2026-07-19T01:14:50.885797Z","pmid":"37214339","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}