Data augmentation using generative networks to identify dementia
This work addresses data scarcity in healthcare AI, specifically for dementia detection, but is incremental as it applies existing generative methods to a new domain.
The paper tackled the problem of limited data for training machine learning classifiers in medical applications by using generative models for data augmentation on speech and audio features for dementia detection, resulting in an F-score improvement from 58% to 74% for a four-way classifier.
Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement