Fig. 5. A typical example of contrastive learning of supervised and self-supervised learning. A: Supervised contrastive learning with positive pairs of follow-up images of the same patient (red dots) and negative pairs of different patients (blue dots). B: Two different augmentations of medical images are randomly performed. Their embedding vectors are obtained through an image encoder to encourage a model to learn similar representations in the same class and dissimilar representations in different classes with similarity loss in a self-supervised manner.