Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pre-training and Fine-tuning Transformers for fMRI Prediction Tasks   We present the TFF Transformer framework for the analysis of functional
Magnetic Resonance Imaging (fMRI) data. TFF employs a transformer-based
architecture and a two-phase training approach. First, self-supervised training
is applied to a collection of fMRI scans, where the model is trained for the
reconstruction of 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.

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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: stat