Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy

• Main Authors: Pellano, Kimji N, Strümke, Inga, Groos, Daniel, Adde, Lars, Ihlen, Espen Alexander F
• Format: Journal Article
• Language: English
• Published: 13-08-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2409.00001

Early detection of Cerebral Palsy (CP) is crucial for effective intervention
and monitoring. This paper tests the reliability and applicability of
Explainable AI (XAI) methods using a deep learning method that predicts CP by
analyzing skeletal data extracted from video recordings of infant movements.
Specifically, we use XAI evaluation metrics -- namely faithfulness and
stability -- to quantitatively assess the reliability of Class Activation
Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in this
specific medical application. We utilize a unique dataset of infant movements
and apply skeleton data perturbations without distorting the original dynamics
of the infant movements. Our CP prediction model utilizes an ensemble approach,
so we evaluate the XAI metrics performances for both the overall ensemble and
the individual models. Our findings indicate that both XAI methods effectively
identify key body points influencing CP predictions and that the explanations
are robust against minor data perturbations. Grad-CAM significantly outperforms
CAM in the RISv metric, which measures stability in terms of velocity. In
contrast, CAM performs better in the RISb metric, which relates to bone
stability, and the RRS metric, which assesses internal representation
robustness. Individual models within the ensemble show varied results, and
neither CAM nor Grad-CAM consistently outperform the other, with the ensemble
approach providing a representation of outcomes from its constituent models.