Systematic comparison of semi-supervised and self-supervised learning for medical image classification

arXiv:2307.08919v3 Announce Type: replace-cross
Abstract: In typical medical image classification problems, labeled data is scarce while unlabeled data is more available. Semi-supervised learning and self-supervised learning are two different research directions that can improve accuracy by learning from extra unlabeled data. Recent methods from both directions have reported significant gains on traditional benchmarks. Yet past benchmarks do not focus on medical tasks and rarely compare self- and semi- methods together on an equal footing. Furthermore, past benchmarks often handle hyperparameter …

abstract accuracy arxiv classification comparison cs.cv cs.lg data extra image medical research self-supervised learning semi-supervised semi-supervised learning supervised learning type