Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the acquisition of valuable insights from readily available unlabeled images. We present Semi-Supervised Relational Contrastive Learning (SRCL), a novel semi-supervised learning model that leverages self-supervised contrastive loss and sample relation consistency for the more meaningful and effective exploitation of unlabeled data. Our experimentation with the SRCL model explores both pre-train/fine-tune and joint learning of the pretext (contrastive learning) and downstream (diagnostic classification) tasks. We validate against the ISIC 2018 Challenge benchmark skin lesion classification dataset and demonstrate the effectiveness of our semi-supervised method on varying amounts of labeled data.

翻译：通过监督学习从医学图像中进行疾病诊断通常依赖于医学专家繁琐、易错且成本高昂的图像标注。然而，半监督学习和自监督学习通过从易于获取的无标签图像中获取有价值的见解而展现出有效性。我们提出半监督关系对比学习（SRCL），一种新型的半监督学习模型，利用自监督对比损失和样本关系一致性来更有效且更有意义地利用无标签数据。我们对SRCL模型的实验探索了预训练/微调以及前置任务（对比学习）与下游任务（诊断分类）的联合学习。我们使用ISIC 2018挑战赛基准皮肤病变分类数据集进行验证，并证明了我们的半监督方法在不同数量的标注数据上的有效性。