PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions
This work addresses pain assessment in laboratory rats, an emerging domain-specific problem, but is incremental as it applies existing deep learning techniques to a new dataset.
The authors tackled the problem of automatically assessing pain in laboratory rats through facial expressions by creating a public dataset of 1,138 images and proposing a deep learning method called PainSeeker, which demonstrated feasibility and effectiveness in experiments.
In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions. To this end, we began by presenting a publicly available dataset called RatsPain, consisting of 1,138 facial images captured from six rats that underwent an orthodontic treatment operation. Each rat' facial images in RatsPain were carefully selected from videos recorded either before or after the operation and well labeled by eight annotators according to the Rat Grimace Scale (RGS). We then proposed a novel deep learning method called PainSeeker for automatically assessing pain in rats via facial expressions. PainSeeker aims to seek pain-related facial local regions that facilitate learning both pain discriminative and head pose robust features from facial expression images. To evaluate the PainSeeker, we conducted extensive experiments on the RatsPain dataset. The results demonstrate the feasibility of assessing rats' pain from their facial expressions and also verify the effectiveness of the proposed PainSeeker in addressing this emerging but intriguing problem. The RasPain dataset can be freely obtained from https://github.com/xhzongyuan/RatsPain.