2024-01-23-rosenman24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Pre-Training Transformers for Fingerprinting to Improve Stress Prediction in fMRI	We harness a Transformer-based model and a pre-training procedure for fingerprinting on fMRI data, to enhance the accuracy of stress predictions. Our model, called MetricFMRI, first optimizes a pixel-based reconstruction loss. In a second unsupervised training phase, a triplet loss is used to encourage fMRI sequences of the same subject to have closer representations, while sequences from different subjects are pushed away from each other. Finally, supervised learning is used for the target task, based on the learned representation. We evaluate the performance of our model and other alternatives and conclude that the triplet training for the fingerprinting task is key to the improved accuracy of our method for the task of stress prediction. To obtain insights regarding the learned model, gradient-based explainability techniques are used, indicating that sub-cortical brain regions that are known to play a central role in stress-related processes are highlighted by the model.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	rosenman24a	0	Pre-Training Transformers for Fingerprinting to Improve Stress Prediction in fMRI	212	234	212-234	212	false	Rosenman, Gony and Malkiel, Itzik and Greental, Ayam and Hendler, Talma and Wolf, Lior	given family Gony Rosenman given family Itzik Malkiel given family Ayam Greental given family Talma Hendler given family Lior Wolf	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/rosenman24a/rosenman24a.pdf