  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.

Computer Vision and Pattern Recognition