On Reproducibility of Graph Neural Network for Facial Palsy and Paresis Assessment: Effects of Pose Variability in Dataset

Zolbayar Shagdar, Seyed Ali Amirshahi, Kiran Raja

Abstract: Reproducibility in terms of implementation and performance is a crucial aspect of healthcare applications as they can have high impact consequences on patient welfare and safety. In this paper, we focus on the consistency and reproduction of results for graph neural networks (GNN) based facial palsy and paresis evaluation. Comparative studies between our proposed GNN-based model and state-of-the-art (SOTA) convolutional neural network-based models suggest that the GNN model is sensitive to pose variability within the dataset while the CNN-based models are consistent across the board. With these findings, we propose a sufficiently regularised dataset with pose variability for obtaining consistent and better results. We provide further analysis of the classification behaviour of our model, the results of which suggest potential label ambiguity within the dataset employed. Future improvements regarding the model’s performance and consistency are recommended based on the reproducibility analyses.

External IDs:doi:10.1007/978-3-031-97822-7_10