Department of Computer Engineering at the University of Texas at Arlington, TX, USA.
Received on 03 March 2021; revised on 18 May 2021; accepted on 29 May 2021
This paper explores the use of self-supervised learning (SSL) for pre-training deep learning models on large-scale, unlabeled medical image datasets. By utilizing surrogate tasks, we improve feature learning in data-scarce environments.
Self-Supervised; Deep Learning; Machine Learning; Artificial Intelligence; LUNA16
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Steve Bartlett, Sridhar Rajan, Ajit Rawat, Mat Yeon and Andy Christie. Self-supervised pre-training of deep learning models for unlabeled medical image datasets. World Journal of Advanced Engineering Technology and Sciences, 2021, 02(02), 100–103. Article DOI: https://doi.org/10.30574/wjaets.2021.2.2.0031