Self-Supervised Transformers for fMRI representationDownload PDF

07 Dec 2021, 09:45 (modified: 06 Jul 2022, 09:23)MIDL 2022Readers: Everyone
Keywords: fMRI, Transformers, Self-supervision.
Abstract: We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase training approach. First, self-supervised training is applied to a collection of fMRI scans, where the model is trained to reconstruct 3D volume data. Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground truth labels. Our results show state-of-the-art performance on a variety of fMRI tasks, including age and gender prediction, as well as schizophrenia recognition. Our code for the training, network architecture, and results is attached as supplementary material.
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Paper Type: both
Primary Subject Area: Unsupervised Learning and Representation Learning
Secondary Subject Area: Learning with Noisy Labels and Limited Data
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