Deep Transfer Learning For Whole-Brain fMRI Analyses
This addresses the challenge of applying deep learning to fMRI data in clinical settings where data is scarce, though it is incremental as it applies an existing transfer learning approach to a specific domain.
The paper tackled the problem of decoding cognitive states from whole-brain fMRI data, which is hindered by small sample sizes and high dimensionality, by using transfer learning from a large dataset to improve performance on new tasks, achieving 67.51% accuracy with fine-tuning on only three subjects.
The application of deep learning (DL) models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data is often hindered by the small sample size and high dimensionality of these datasets. Especially, in clinical settings, where patient data are scarce. In this work, we demonstrate that transfer learning represents a solution to this problem. Particularly, we show that a DL model, which has been previously trained on a large openly available fMRI dataset of the Human Connectome Project, outperforms a model variant with the same architecture, but which is trained from scratch, when both are applied to the data of a new, unrelated fMRI task. Even further, the pre-trained DL model variant is already able to correctly decode 67.51% of the cognitive states from a test dataset with 100 individuals, when fine-tuned on a dataset of the size of only three subjects.