ArticleExpert systems with applications2025
Deep video anomaly detection in automated laboratory setting.
Article in Expert systems with applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- TriPath3DNet: an efficient real-time model for multi-class classification in real-life surveillance videos of fixed duration.Scientific reports · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Laboratory automation integrates robotics, machine learning, and computer vision to enhance precision and efficiency while reducing costs. Despite its pivotal importance in fully automated experimentation, automatic monitoring of procedures is an overlooked task. In this paper, we aim to address this shortcoming by developing a learning method for detecting anomalies in the laboratory setting. To formalize the problem, we focus on the liquid transfer task as a major task in laboratory automation, given the frequent need for reagent mixing and solution preparation. We introduce a novel video anomaly detection framework that leverages the robust CLIP features in conjunction with a transformer encoder. Through an array of experiments and ablation studies, we demonstrate that our proposed method surpasses current state-of-the-art anomaly detection techniques adapted to laboratory automation by a notable margin of over 11%, achieving an impressive AUC of 98.79% for video-level anomaly detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.