Leveraging machine learning for approaching automated pre-clinical rodent models

This thesis evaluates deep learning architectures for rats pose estimation through the presented six-camera system, focusing on ResNet and EfficientNet models via different depth and augmentation techniques. Among the configurations tested, ResNet 152 with default augmentation proved the best performance in the controlled experimental setup, particularly when employing a multi-perspective network approach. It reached a Root Mean Squared Error (RMSE) of 8.74, 8.78, and 9.72 pixels for the different angles. This configuration not only minimized test errors but also demonstrated its ability to track animal movement consistently. The utilization of data augmentation revealed that less altering yields better performance given the experimental conditions. The thesis suggests potential areas for future directions, including extended refinement of model configurations, further investigation of inference speed, and utilization of the network weights to transfer the approach to other species, such as mice. The findings underscore the potential for further development of deep learning solutions advancing pre-clinical research in behavioral neuroscience.