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In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge ofdoi:10.5815/ijigsp.2021.04.03 fatcat:ff7ybaplqncthgswf3zy7cbeza