Katharina Hohlbaum

This paper evaluates three computer vision paradigms for automated mouse well-being assessment using the Mouse Grimace Scale and finds they achieve low error rates and use relevant visual features.

GrimACE is an automated tool for assessing pain and well-being in mice using computer vision, showing high correlation with expert evaluations and detecting pain and treatment effects.

This paper introduces a deep learning model for automated pain face analysis in mice using the Mouse Grimace Scale, achieving high correlation with human raters and outperforming them in error magnitude.

This study uses a lockbox task to show that mice primarily develop motor skills before adopting a cognitive strategy for solving a complex mechanical puzzle.