arXiv Open Access 2021

Self-Supervised Transformers for fMRI representation

Itzik Malkiel Gony Rosenman Lior Wolf Talma Hendler

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Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV cs.LG

Penulis (4)

Itzik Malkiel

Gony Rosenman

Lior Wolf

Talma Hendler

Format Sitasi

APA MLA BibTeX

Malkiel, I., Rosenman, G., Wolf, L., Hendler, T. (2021). Self-Supervised Transformers for fMRI representation. https://arxiv.org/abs/2112.05761

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓