April 5, 2024, 4:45 a.m. | Chuang Li, Shuai Shao, Willian Mikason, Rubing Lin, Yantong Liu

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.03121v1 Announce Type: new
Abstract: The demand for improved efficiency and accuracy in vaccine safety assessments is increasing. Here, we explore the application of computer vision technologies to automate the monitoring of experimental mice for potential side effects after vaccine administration. Traditional observation methods are labor-intensive and lack the capability for continuous monitoring. By deploying a computer vision system, our research aims to improve the efficiency and accuracy of vaccine safety assessments. The methodology involves training machine learning models on …

abstract accuracy administration application arxiv automate computer computer vision continuous continuous monitoring cs.cv demand effects efficiency experimental explore labor monitoring observation q-bio.nc safety technologies type vaccine vision

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US