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Deep Transfer Learning for Whole-Brain FMRI Analyses

: Thomas, A.W.; Müller, K.-R.; Samek, W.


Zhou, L.:
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging : Second International Workshop, OR 2.0 2019 and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11796)
ISBN: 978-3-030-32694-4 (Print)
ISBN: 978-3-030-32695-1 (Online)
International Workshop on Context-Aware Surgical Theaters <2, 2019, Shenzhen>
International Workshop on Machine Learning in Clinical Neuroimaging (MLCN) <2, 2019, Shenzhen>
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <22, 2019, Shenzhen>
Fraunhofer HHI ()

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. The pre-trained DL model variant is 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.