Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
This work addresses data scarcity in neuroimaging for researchers developing deep learning models, though it is incremental as it applies existing LSTM methods to a specific domain.
The paper tackled the problem of limited neuroimaging datasets for deep learning by proposing an LSTM-based dynamic forecasting framework for data augmentation, which improved model performance in chronological age prediction tasks.
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.