Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention
This work addresses the need for interpretable deep learning models in clinical settings for early cerebral palsy diagnosis, offering a domain-specific improvement.
The paper tackles the problem of predicting cerebral palsy from infant body movements using deep learning, achieving 91.67% accuracy and providing interpretability through a channel attention module that highlights key body joints.
Early prediction of cerebral palsy is essential as it leads to early treatment and monitoring. Deep learning has shown promising results in biomedical engineering thanks to its capacity of modelling complicated data with its non-linear architecture. However, due to their complex structure, deep learning models are generally not interpretable by humans, making it difficult for clinicians to rely on the findings. In this paper, we propose a channel attention module for deep learning models to predict cerebral palsy from infants' body movements, which highlights the key features (i.e. body joints) the model identifies as important, thereby indicating why certain diagnostic results are found. To highlight the capacity of the deep network in modelling input features, we utilize raw joint positions instead of hand-crafted features. We validate our system with a real-world infant movement dataset. Our proposed channel attention module enables the visualization of the vital joints to this disease that the network considers. Our system achieves 91.67% accuracy, suppressing other state-of-the-art deep learning methods.