labmouse

Automated Pain Assessment in Laboratory Mice using YOLO and Deep Learning

A two-stage deep learning system for automated detection and classification of pain levels in laboratory mice using the Mouse Grimace Scale (MGS). This project combines YOLO object detection for face localization with ResNet-18 classification to assess pain levels from facial expressions.

Overview

This system addresses the challenge of objective pain assessment in laboratory animals by:

• Detecting mouse faces in images using a simplified YOLO architecture
• Classifying pain levels (0-2 on MGS scale) using transfer learning with ResNet-18

Technical Architecture

Stage 1: Face Detection (YOLO)

• Simplified YOLO implementation with 5 convolutional blocks
• Input: 416×416 images → Output: 13×13 detection grid
• 3 anchor boxes optimized for mouse face sizes

Stage 2: Pain Classification (ResNet-18)

• Transfer learning from ImageNet
• Custom classification head with dropout regularization
• Input: 224×224 face crops → Output: 3-class pain predictions

Installation

git clone https://github.com/username/labmouse
cd labmouse
pip install -r requirements.txt

Project Structure

labmouse/
├── data/                          # raw dataset files
├── docs/                          # project's official documentation
├── Eyes_detection/                # eye dataset (1)
├── Face_detection/                # face dataset (2)
├── metrics/                       # training metrics and evaluation
│   └── plots/                     
├── models/                        # trained model weights
├── notebooks/                     

Dataset

• 3,309 images from the White Furred Mice Dataset
• 8 individual mice with manual MGS annotations
• Data augmentation: horizontal flipping, scaling, HSV jittering
• Split: 80% training, 20% validation

Results

• YOLO Detection: Loss reduced from ~8 to ~2 over 5 epochs
• Pain Classification: 84% training accuracy, with good face localization
• Real-time Performance: Suitable for automated monitoring systems

Mouse Grimace Scale (MGS)

The system evaluates pain based on facial features:

• Eyes: Orbital tightening and squinting
• Nose/Cheeks: Bulging and distortion
• Ears: Position changes
• Whiskers: Positioning alterations

Usage

from SimpleYOLO import SimpleYOLO
from PainClassifier import PainClassifier

yolo_model = SimpleYOLO()
pain_classifier = PainClassifier()

yolo_model.load_state_dict(torch.load('models/yolo_model.pth'))
pain_classifier.load_state_dict(torch.load('models/best_classifier.pth'))

# image = ... sth sth

detections = yolo_model(image)

# cropped_face = ... sth sth

pain_level = pain_classifier(cropped_face)

Scientific Background

This implementation is based on the Mouse Grimace Scale developed by Langford et al. (2010), providing an objective method for pain assessment that:

• Reduces observer bias
• Enables continuous monitoring
• Improves animal welfare in research settings

Acknowledgments

• Dataset: A. Vidal, S. Jha, S. Hassler, T. Price, C. Busso (2022)
  • White Furred Mice Dataset
  • GitHub Repository
• Original MGS: Langford et al. (2010), Nature Methods
• Architectures: YOLO (Redmon et al., 2016), ResNet (He et al., 2016)

License

MIT